Method and system for computer-aided triage of stroke

ABSTRACT

A system for computer-aided triage includes a router, a remote computing system, and a client application. Additionally or alternatively, the system  100  can include any number of computing systems, servers, storage, lookup table, memory, and/or any other suitable components. A method for computer-aided triage includes receiving a data packet associated with a patient and taken at a first point of care; checking for a suspected condition associated with the data packet; in an event that the suspected condition is detected, determining a recipient based on the suspected condition; and transmitting information to a device associated with the recipient.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is continuation of U.S. application Ser. No.16/913,754, filed 26 Jun. 2020, which claims the benefit of U.S.Provisional Application No. 62/867,566, filed 27 Jun. 2019, each ofwhich is incorporated herein in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the medical diagnostic field, andmore specifically to a new and useful system and method forcomputer-aided triage in the medical diagnostic field.

BACKGROUND

In current triaging workflows, especially those in an emergency setting,a patient presents at a first point of care, where an assessment, suchas imaging, is performed. The image data is then sent to a standardradiology workflow, which typically involves: images being uploaded to aradiologist's queue, the radiologist reviewing the images at aworkstation, the radiologist generating a report, an emergencydepartment doctor reviewing the radiologist's report, the emergencydepartment doctor determining and contact a specialist, and making adecision of how to treat and/or transfer the patient to a 2^(nd) pointof care. This workflow is typically very time-consuming, which increasesthe time it takes to treat and/or transfer a patient to a specialist. Inmany conditions, especially those involving stroke, time is extremelysensitive, as it is estimated that in the case of stroke, a patientloses about 1.9 million neurons per minute that the stroke is leftuntreated (Saver et al.). Further, as time passes, the amount and typesof treatment options available to the patient decrease.

Thus, there is a need in the triaging field to create an improved anduseful system and method for decreasing the time it takes to determineand initiate treatment for a patient presenting with a criticalcondition.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a system for computer-aided triage.

FIG. 2 is a schematic of a method for computer-aided triage.

FIG. 3 depicts a variation of a method for computer-aided triage.

FIG. 4 depicts a variation of determining a patient condition during amethod for computer-aided triage.

FIGS. 5A and 5B depict a variation of an application on a user device.

FIG. 6 depicts a variation of a method for computer-aided triage.

FIG. 7 depicts a variation of a method for computer-aided triage.

FIG. 8 depicts a variation of an annotated image transmitted to arecipient.

FIG. 9 depicts a variation of the method.

FIG. 10 depicts a variation of the method.

FIG. 11 depicts a variation of the method involving recommending thepatient for a clinical trial.

FIG. 12 depicts a variation of a notification transmitted to a device ofa participant.

FIG. 13 depicts a variation of a notification and subsequent workflow ofrecommending a patient for a clinical trial.

FIG. 14 depicts a variation of the system.

FIG. 15 depicts variations of a client application executing on a firstand second user device.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1 , a system 100 for computer-aided triage includes arouter, a remote computing system, and a client application.Additionally or alternatively, the system 100 can include any number ofcomputing systems (e.g., local, remote), servers (e.g., PACS server),storage, lookup table, memory, and/or any other suitable components.Further additionally or alternatively, the system can include any or allof the components, embodiments, and examples as described in any or allof: U.S. application Ser. No. 16/012,458, filed 19 Jun. 2018; U.S.application Ser. No. 16/012,495, filed 19 Jun. 2018; and U.S.application Ser. No. 16/688,721, filed 19 Nov. 2019, each of which isincorporated in its entirety by this reference.

As shown in FIG. 2 , a method 200 for computer-aided triage includesreceiving a data packet associated with a patient and taken at a firstpoint of care S205; checking for a suspected condition associated withthe data packet S220; in an event that the suspected condition isdetected, determining a recipient based on the suspected condition S230;and transmitting information to a device associated with the recipientS250. Additionally or alternatively, the method 200 can include any orall of: transmitting data to a remote computing system S208; preparing adata packet for analysis S210; determining a parameter associated with adata packet; determining a treatment option based on the parameter;preparing a data packet for transfer S240; receiving an input from therecipient; initiating treatment of the patient; transmitting informationto a device associated with a second point of care S150; and/or anyother suitable processes. Further additionally or alternatively, themethod 200 can include any or all of the processes, embodiments, andexamples described in any or all of: U.S. application Ser. No.16/012,458, filed 19 Jun. 2018; U.S. application Ser. No. 16/012,495,filed 19 Jun. 2018; and U.S. application Ser. No. 16/688,721, filed 19Nov. 2019, each of which is incorporated in its entirety by thisreference, or any other suitable processes performed in any suitableorder. The method 200 can be performed with a system as described aboveand/or any other suitable system.

2. Benefits

The system and method for computer-aided triage can confer severalbenefits over current systems and methods.

In a first variation, the system and/or method confer the benefit ofreducing the time to match and/or transfer a patient presenting with acondition (e.g., hemorrhage, intracerebral hemorrhage [ICH]) to aspecialist. In a specific example, for instance, the average timebetween generating a non-contrast dataset and notifying a specialist(e.g., in a case associated with a suspected condition) is reduced fromover 30 minutes (e.g., 35 minutes, 40 minutes, 45 minutes, 50 minutes,greater than 50 minutes, etc.) to less than 8 minutes (e.g., 30 seconds,less than a minute, between 1-2 minutes, 2 minutes, between 2-3 minutes,3 minutes, between 3-4 minutes, etc.).

In a second variation, the system and/or method provide a parallelprocess to a traditional workflow (e.g., standard radiology workflow),which can confer the benefit of reducing the time to determine atreatment option while having the outcome of the traditional workflow asa backup in the case that an inconclusive or inaccurate determination(e.g., false negative, false positive, etc.) results from the method.Additionally or alternatively, the system and/or method can beimplemented in place of and/or integrated within a traditional workflow(e.g., the standard radiology workflow).

In a third variation, the system and/or method is configured to have ahigh sensitivity (e.g., 87.8%, approximately 88%, between 81% and 93%,greater than 87%, etc.), which functions to detect a high number of truepositive cases and help these patients reach treatment faster. In theevent that this results in a false positive, only a minor disturbance—ifany—is caused to a specialist, which affects the specialist's workflownegligibly (e.g., less than 5 minutes), if at all. In a specificexample, the method is configured to have a sensitivity above apredetermined threshold after a particular category of artifacts (e.g.,motion artifacts, metal artifacts, etc.) have been checked for.

In a fourth variation, the system and/or method confer the benefit ofminimizing the occurrence of false positive cases (e.g., less than 10%occurrence, less than 5% occurrence, less than, which functions tominimize disturbances caused to specialists or other individuals. Thiscan function to minimize unnecessary disturbances to specialists invariations in which specialists or other users are notified on a mobiledevice upon detection of a potential brain condition (e.g., ICH), as itcan minimize the occurrence of a specialist being alerted (e.g.,potentially at inconvenient times of day, while the specialist isotherwise occupied, etc.) for false positives, while still maintaining afallback in the standard radiology workflow in the event that a truepositive is missed. In a set of specific examples, the method includestraining (e.g., iteratively training) a set of deep learning modelsinvolved in ICH detection on images originally detected to be a truepositive but later identified as a false positive.

In a fifth variation, the system and/or method confer the benefit ofreorganizing a queue of patients, wherein patients having a certaincondition are detected early and prioritized (e.g., moved to the frontof the queue).

In a sixth variation, the system and/or method confer the benefit ofdetermining actionable analytics to optimize a workflow, such as anemergency room triage workflow.

In a seventh variation, the system and/or method confer the benefit ofautomatically determining a potential hemorrhage (e.g., ICH) in a set ofnon-contrast scans and notifying a specialist prior to theidentification of a potential ICH by a radiologist during a parallelworkflow.

In an eighth variation, the system and/or method confer the benefit ofrecommending a patient for a clinical trial based on an automatedprocessing of a set of images (e.g., brain images) associated with thepatient.

In a ninth variation, the system and/or method confer the benefit ofdetermining a suspected patient condition with a sensitivity of at least95% (e.g., 96%, 97%, between 96% and 97%, etc.) and a specificity of atleast 94% (e.g., 96%, 97%, between 96% and 97%, etc.).

Additionally or alternatively, the system and method can confer anyother benefit.

3. System

The system preferably interfaces with one or more points of care (e.g.,1^(st) point of care, 2^(nd) point of care, 3^(rd) point of care, etc.),each of which are typically a healthcare facility. A 1^(st) point ofcare herein refers to the healthcare facility at which a patientpresents, typically where the patient first presents (e.g., in anemergency setting). Conventionally, healthcare facilities include spokefacilities, which are often general (e.g., non-specialist, emergency,etc.) facilities, and hub (e.g., specialist) facilities, which can beequipped or better equipped (e.g., in comparison to spoke facilities)for certain procedures (e.g., mechanical thrombectomy), conditions(e.g., stroke), or patients (e.g., high risk). Patients typicallypresent to a spoke facility at a 1^(st) point of care, but canalternatively present to a hub facility, such as when it is evident whatcondition their symptoms reflect, when they have a prior history of aserious condition, when the condition has progressed to a high severity,when a hub facility is closest, randomly, or for any other reason. Ahealthcare facility can include any or all of: a hospital, clinic,ambulances, doctor's office, imaging center, laboratory, primary strokecenter (PSC), comprehensive stroke center (CSC), stroke ready center,interventional ready center, or any other suitable facility involved inpatient care and/or diagnostic testing.

A patient can be presenting with symptoms of a condition, no symptoms(e.g., presenting for routine testing), or for any other suitablesystem. In some variations, the patient is presenting with one or morestroke symptoms (e.g., hemorrhagic stroke symptoms), such as, but notlimited to: weakness, numbness, speech abnormalities, and facialdrooping. Typically, these patients are then treated in accordance witha stroke protocol, which typically involves an imaging protocol at animaging modality, such as, but not limited to: a non-contrast CT (NCCT)scan of the head, a CTA scan of the head and neck, a CT perfusion (CTP)scan of the head.

A healthcare worker herein refers to any individual or entity associatedwith a healthcare facility, such as, but not limited to: a physician,emergency room physician (e.g., orders appropriate lab and imaging testsin accordance with a stroke protocol), radiologist (e.g., on-dutyradiologist, healthcare worker reviewing a completed imaging study,healthcare working authoring a final report, etc.), neuroradiologist,specialist (e.g., neurovascular specialist, vascular neurologist,neuro-interventional specialist, neuro-endovascular specialist,expert/specialist in a procedure such as mechanical thrombectomy,cardiac specialist, etc.), administrative assistant, healthcare facilityemployee (e.g., staff employee), emergency responder (e.g., emergencymedical technician), or any other suitable individual.

The image data can include computed tomography (CT) data (e.g.,radiographic CT, non-contrast CT, CT perfusion, etc.), preferablynon-contrast CT data (e.g., axial data, axial series of slices,consecutive slices, etc.) but can additionally or alternatively anyother suitable image data. The image data is preferably generated at animaging modality (e.g., scanner at the 1^(st) point of care), such as aCT scanner, magnetic resonance imaging (MRI) scanner, ultrasound system,or any other scanner. Additionally or alternatively, image data can begenerated from a camera, user device, accessed from a database orweb-based platform, drawn, sketched, or otherwise obtained.

3.1 System—Router 110

The system 100 can include a router no (e.g., medical routing system),which functions to receive a data packet (e.g., dataset) includinginstances (e.g., images, scans, etc.) taken at an imaging modality(e.g., scanner) via a computing system (e.g., scanner, workstation, PACSserver) associated with a 1^(st) point of care. The instances arepreferably in the Digital Imaging and Communications in Medicine (DICOM)file format, as well as generated and transferred between computingsystem in accordance with a DICOM protocol, but can additionally oralternatively be in any suitable format. Additionally or alternatively,the instances can include any suitable medical data (e.g., diagnosticdata, patient data, patient history, patient demographic information,etc.), such as, but not limited to, PACS data, Health-Level 7 (HL7)data, electronic health record (EHR) data, or any other suitable data,and to forward the data to a remote computing system.

The instances preferably include (e.g., are tagged with) and/orassociated with a set of metadata, but can additionally or alternativelyinclude multiple sets of metadata, no metadata, extracted (e.g.,removed) metadata (e.g., for regulatory purposes, HIPAA compliance,etc.), altered (e.g., encrypted, decrypted, deidentified, anonymizedetc.) metadata, or any other suitable metadata, tags, identifiers, orother suitable information.

The router 110 can refer to or include a virtual entity (e.g., virtualmachine, virtual server, etc.) and/or a physical entity (e.g., localserver). The router can be local (e.g., at a 1^(st) healthcare facility,2^(nd) healthcare facility, etc.) and associated with (e.g., connectedto) any or all of: on-site server associated with any or all of theimaging modality, the healthcare facility's PACS architecture (e.g.,server associated with physician workstations), or any other suitablelocal server or DICOM compatible device(s). Additionally oralternatively, the router can be remote (e.g., locate at a remotefacility, remote server, cloud computing system, etc.), and associatedwith any or all of: a remote server associated with the PACS system, amodality, or another DICOM compatible device such as a DICOM router.

The router 110 preferably operates on (e.g., is integrated into) asystem (e.g., computing system, workstation, server, PACS server,imaging modality, scanner, etc.) at a 1^(st) point of care butadditionally or alternatively, at a 2^(nd) point of care, remote server(e.g., physical, virtual, etc.) associated with one or both of the1^(st) point of care and the 2^(nd) point of care (e.g., PACS server,EHR server, HL7 server), a data storage system (e.g., patient records),or any other suitable system. In some variations, the system that therouter operates on is physical (e.g., physical workstation, imagingmodality, scanner, etc.) but can additionally or alternatively includevirtual components (e.g., virtual server, virtual database, cloudcomputing system, etc.).

The router 110 is preferably configured to receive data (e.g.,instances, images, study, series, etc.) from an imaging modality,preferably an imaging modality (e.g., CT scanner, MRI scanner,ultrasound machine, etc.) at a first point of care (e.g., spoke, hub,etc.) but can additionally or alternatively be at a second point of care(e.g., hub, spoke, etc.), multiple points of care, or any otherhealthcare facility. The router can be coupled in any suitable way(e.g., wired connection, wireless connection, etc.) to the imagingmodality (e.g., directly connected, indirectly connected via a PACSserver, etc.). Additionally or alternatively, the router can beconnected to the healthcare facility's PACS architecture, or otherserver or DICOM-compatible device of any point of care or healthcarefacility.

In some variations, the router includes a virtual machine operating on acomputing system (e.g., computer, workstation, user device, etc.),imaging modality (e.g., scanner), server (e.g., PACS server, server at1^(st) healthcare facility, server at 2^(nd) healthcare facility, etc.),or other system. In a specific example, the router is part of a virtualmachine server. In another specific example, the router is part of alocal server.

3.2 System—Remote Computing System 120

The system 100 can include a remote computing system 120, which canfunction to receive and process data packets (e.g., dataset fromrouter), determine a treatment option (e.g., select a 2^(nd) point ofcare, select a specialist, etc.), interface with a user device (e.g.,mobile device), compress a data packet, extract and/or remove metadatafrom a data packet (e.g., to comply with a regulatory agency), orperform any other suitable function.

Preferably, part of the method 200 is performed at the remote computingsystem (e.g., cloud-based), but additionally or alternatively all of themethod 200 can be performed at the remote computing system, the method200 can be performed at any other suitable computing system(s). In somevariations, the remote computing system 120 provides an interface fortechnical support (e.g., for a client application) and/or analytics. Insome variations, the remote computing system includes storage and isconfigured to store and/or access a lookup table, wherein the lookuptable functions to determine a treatment option (e.g., 2^(nd) point ofcare), a contact associated with the 2^(nd) point of care, and/or anyother suitable information.

In some variations, the remote computing system 120 connects multiplehealthcare facilities (e.g., through a client application, through amessaging platform, etc.).

In some variations, the remote computing system 120 functions to receiveone or more inputs and/or to monitor a set of client applications (e.g.,executing on user devices, executing on workstations, etc.).

3.3 System—Application 130

The system 100 can include one or more applications 130 (e.g., clients,client applications, client application executing on a device, etc.),such as the application shown in FIGS. 5A and 5B, which individually orcollectively function to provide one or more outputs (e.g., from theremote computing system) to a contact. Additionally or alternatively,the applications can individually or collectively function to receiveone or more inputs from a contact, provide one or more outputs to ahealthcare facility (e.g., first point of care, second point of care,etc.), establish communication between healthcare facilities, or performany other suitable function.

In some variations, one or more features of the application (e.g.,appearance, information content, information displayed, user interface,graphical user interface, etc.) are determined based on any or all of:the type of device that the application is operating on (e.g., userdevice vs. healthcare facility device, mobile device vs. stationarydevice), where the device is located (e.g., 1^(st) point of care, 2^(nd)point of care, etc.), who is interacting with the application (e.g.,user identifier, user security clearance, user permission, etc.), or anyother characteristic. In some variations, for instance, an applicationexecuting on a healthcare facility will display a 1^(st) set ofinformation (e.g., uncompressed images, metadata, etc.) while anapplication executing on a user device will display a 2^(nd) set ofinformation (e.g., compressed images, no metadata, etc.). In somevariations, the type of data to display is determined based on any orall of: an application identifier, mobile device identifier, workstationidentifier, or any other suitable identifier.

The outputs of the application can include any or all of: an alert ornotification (e.g., push notification, text message, call, email, etc.);an image set (e.g., compressed version of images taken at scanner,preview of images taken at scanner, images taken at scanner, etc.); aset of tools for interacting with the image set, such as any or all ofpanning, zooming, rotating, adjusting window level and width, scrolling,performing maximum intensity projection [MIP] (e.g., option to selectthe slab thickness of a MIP), changing the orientation of a 3D scan(e.g., changing between axial, coronal, and sagittal views, freestyleorientation change), showing multiple views of a set of images; aworklist (e.g., list of patients presenting for and/or requiring care,patients being taken care of by specialist, patients recommended tospecialist, procedures to be performed by specialist, etc.); a messagingplatform (e.g., HIPAA-compliant messaging platform, texting platform,video messaging, group messaging etc.); a telecommunication platform(e.g., video conferencing platform); a directory of contact information(e.g., 1^(st) point of care contact info, 2^(nd) point of care contactinfo, etc.); tracking of a workflow or activity (e.g., real-time or nearreal-time updates of patient status/workflow/etc.); analytics based onor related to the tracking (e.g., predictive analytics such as predictedtime remaining in radiology workflow or predicted time until strokereaches a certain severity; average time in a workflow; average time totransition to a second point of care, etc.); or any other suitableoutput.

The inputs can include any or all of the outputs described previously,touch inputs (e.g., received at a touch-sensitive surface), audioinputs, optical inputs, or any other suitable input. The set of inputspreferably includes an input indicating receipt of an output by arecipient (e.g., read receipt of a specialist upon opening anotification). This can include an active input from the contact (e.g.,contact makes selection at application), a passive input (e.g., readreceipt), or any other input.

In one variation, the system 100 includes a mobile device application130 and a workstation application 130—both connected to the remotecomputing system—wherein a shared user identifier (e.g., specialistaccount, user account, etc.) can be used to connect the applications(e.g., retrieve a case, image set, etc.) and determine the informationto be displayed at each application (e.g., variations of imagedatasets). In one example, the information to be displayed (e.g.,compressed images, high-resolution images, etc.) can be determined basedon: the system type (e.g., mobile device, workstation), the applicationtype (e.g., mobile device application, workstation application), theuser account (e.g., permissions, etc.), any other suitable information,or otherwise determined.

The application can include any suitable algorithms or processes foranalysis, and part or all of the method 200 can be performed by aprocessor associated with the application.

The application preferably includes both front-end (e.g., applicationexecuting on a user device, application executing on a workstation,etc.) and back-end components (e.g., software, processing at a remotecomputing system, etc.), but can additionally or alternatively includejust front-end or back-end components, or any number of componentsimplemented at any suitable system(s).

3.4 System—Additional Components

The system 100 and/or or any component of the system 100 can optionallyinclude or be coupled to any suitable component for operation, such as,but not limited to: a processing module (e.g., processor,microprocessor, etc.), control module (e.g., controller,microcontroller), power module (e.g., power source, battery,rechargeable battery, mains power, inductive charger, etc.), sensorsystem (e.g., optical sensor, camera, microphone, motion sensor,location sensor, etc.), or any other suitable component.

4. Method

As shown in FIG. 2 , the method 200 includes receiving a data packetassociated with a patient and taken at a first point of care S205;checking for a suspected condition associated with the data packet S220;in an event that the suspected condition is detected, determining arecipient based on the suspected condition S230; and transmittinginformation to a device associated with the recipient S250. Additionallyor alternatively, the method 200 can include any or all of: transmittingdata to a remote computing system S208; preparing a data packet foranalysis S210; determining a parameter associated with a data packet;determining a treatment option based on the parameter; preparing a datapacket for transfer S240; receiving an input from the recipient;initiating treatment of the patient; transmitting information to adevice associated with a second point of care S150; and/or any othersuitable processes.

The method 200 is preferably performed separate from but in parallelwith (e.g., contemporaneously with, concurrently with, etc.) a standardradiology workflow (e.g., as shown in FIG. 3 ), but can additionally oralternatively be implemented within a standard workflow, be performed ata separate time with respect to a standard workflow, or be performed atany suitable time.

The method 200 can be partially or fully implemented with the system 100or with any other suitable system.

The method 200 functions to improve communication across healthcarefacility networks (e.g., stroke networks, spokes and hubs, etc.) anddecrease the time required to transfer a patient having a suspectedtime-sensitive condition (e.g., brain condition, stroke, hemorrhagicstroke, hemorrhage, intracerebral hemorrhage (ICH), ischemic stroke,large vessel occlusion (LVO), cardiac event, trauma, etc.) from a firstpoint of care (e.g., spoke, non-specialist facility, stroke center,ambulance, etc.) to a second point of care (e.g., hub, specialistfacility, comprehensive stroke center, etc.), wherein the second pointof care refers to a healthcare facility equipped to treat the patient.In some variations, the second point of care is the first point of care,wherein the patient is treated at the healthcare facility to which he orshe initially presents.

The method 200 preferably functions as a parallel workflow tool, whereinthe parallel workflow is performed contemporaneously with (e.g.,concurrently, during, partially during) a standard radiology workflow(e.g., radiologist queue), but can additionally or alternatively beimplemented within a standard workflow (e.g., to automate part of astandard workflow process, decrease the time required to perform astandard workflow process, etc.), be performed during a workflow otherthan a radiology workflow (e.g., during a routine examination workflow),or at any other suitable time.

The method 200 is preferably performed in response to a patientpresenting at a first point of care. The first point of care can be anemergency setting (e.g., emergency room, ambulance, imaging center,etc.) or any suitable healthcare facility, such as those describedpreviously. The patient is typically presenting with (or suspected to bepresenting with), equivalently referred to herein as an acute setting, atime-sensitive condition, such as a neurovascular condition (e.g.,stroke, ischemic stroke, occlusion, large vessel occlusion (LVO),thrombus, aneurysm, etc.), cardiac event or condition (e.g.,cardiovascular condition, heart attack, etc.), trauma (e.g., acutetrauma, blood loss, etc.), or any other time-sensitive (e.g.,life-threatening) condition. In other variations, the method isperformed for a patient presenting to a routine healthcare setting(e.g., non-emergency setting, clinic, imaging center, etc.), such as forroutine testing, screening, diagnostics, imaging, clinic review,laboratory testing (e.g., blood tests), or for any other reason.

Any or all of the method can be performed using any number of machinelearning (e.g., deep learning) or computer vision modules. Each modulecan utilize one or more of: supervised learning (e.g., using logisticregression, using back propagation neural networks, using randomforests, decision trees, etc.), unsupervised learning (e.g., using anApriori algorithm, using K-means clustering), semi-supervised learning,reinforcement learning (e.g., using a Q-learning algorithm, usingtemporal difference learning), and any other suitable learning style.Each module of the plurality can implement any one or more of: aregression algorithm (e.g., ordinary least squares, logistic regression,stepwise regression, multivariate adaptive regression splines, locallyestimated scatterplot smoothing, etc.), an instance-based method (e.g.,k-nearest neighbor, learning vector quantization, self-organizing map,etc.), a regularization method (e.g., ridge regression, least absoluteshrinkage and selection operator, elastic net, etc.), a decision treelearning method (e.g., classification and regression tree, iterativedichotomiser 3, C4.5, chi-squared automatic interaction detection,decision stump, random forest, multivariate adaptive regression splines,gradient boosting machines, etc.), a Bayesian method (e.g., naïve Bayes,averaged one-dependence estimators, Bayesian belief network, etc.), akernel method (e.g., a support vector machine, a radial basis function,a linear discriminate analysis, etc.), a clustering method (e.g.,k-means clustering, expectation maximization, etc.), an associated rulelearning algorithm (e.g., an Apriori algorithm, an Eclat algorithm,etc.), an artificial neural network model (e.g., a Perceptron method, aback-propagation method, a Hopfield network method, a self-organizingmap method, a learning vector quantization method, etc.), a deeplearning algorithm (e.g., a restricted Boltzmann machine, a deep beliefnetwork method, a convolution network method, a stacked auto-encodermethod, etc.), a dimensionality reduction method (e.g., principalcomponent analysis, partial lest squares regression, Sammon mapping,multidimensional scaling, projection pursuit, etc.), an ensemble method(e.g., boosting, boostrapped aggregation, AdaBoost, stackedgeneralization, gradient boosting machine method, random forest method,etc.), and any suitable form of machine learning algorithm. Each modulecan additionally or alternatively be a: probabilistic module, heuristicmodule, deterministic module, or be any other suitable module leveragingany other suitable computation method, machine learning method, orcombination thereof.

Each module can be validated, verified, reinforced, calibrated, orotherwise updated based on newly received, up-to-date measurements; pastmeasurements recorded during the operating session; historicmeasurements recorded during past operating sessions; or be updatedbased on any other suitable data. Each module can be run or updated:once; at a predetermined frequency; every time the method is performed;every time an unanticipated measurement value is received; or at anyother suitable frequency. The set of modules can be run or updatedconcurrently with one or more other modules, serially, at varyingfrequencies, or at any other suitable time. Each module can bevalidated, verified, reinforced, calibrated, or otherwise updated basedon newly received, up-to-date data; past data or be updated based on anyother suitable data. Each module can be run or updated: in response todetermination of an actual result differing from an expected result; orat any other suitable frequency.

4.1 Method—Receiving a Data Packet Associated with a Patient and Takenat a First Point of Care S205

The method 200 includes receiving a data packet associated with apatient and taken at a first point of care S205, which functions tocollect data relevant to assessing a patient condition.

The data is preferably received at a router no, wherein the router is inthe form of a virtual machine operating on a computing system (e.g.,computer, workstation, quality assurance (QA) workstation, readingworkstation, PACS server, etc.) coupled to or part of an imagingmodality (e.g., CT scanner, MRI scanner, etc.), or any other suitablerouter. Additionally or alternatively, data can be received at a remotecomputing system (e.g., from an imaging modality, from a database, aserver such as a PACS server, an internet search, social media, etc.),or at any other suitable computing system (e.g., server) or storage site(e.g., database). In some variations, for instance, a subset of the data(e.g., image data) is received at the router while another subset of thedata (e.g., patient information, patient history, etc.) is received at aremote computing system. In a specific example, the data subset receivedat the router is eventually transmitted to the remote computing systemfor analysis.

S205 is preferably performed in response to (e.g., after, in real timewith, substantially in real time with, with a predetermined delay, witha delay of less than 10 seconds, with a delay of less than 1 minute, atthe prompting of a medical professional, etc.) the data (e.g., each of aset of instances) being generated at the imaging modality. Additionallyor alternatively, S205 can be performed in response to a set of multipleinstances being generated by the imaging modality (e.g., after a partialseries has been generated, after a full series has been generated, aftera study has been generated, etc.), in response to a metadata tag beinggenerated (e.g., for an instance, for a series, for a study, etc.), inresponse to a trigger (e.g., request for images), throughout the method(e.g., as a patient's medical records are accessed, as information isentered a server, as information is retrieved from a server, etc.), orat any other suitable time.

S205 can be performed a single time or multiple times (e.g.,sequentially, at different times in the method, once patient conditionhas progressed, etc.). In one variation, each instance is received(e.g., at a router, at a remote computing system, etc.) individually asit is generated. In a second variation, a set of multiple instances(e.g., multiple images, full series, etc.) are received together (e.g.,after a scan has completed, after a particular anatomical component hasbeen imaged, etc.).

The router (e.g., virtual machine, virtual server, application runningon the image sampling system or a computing system connected to theimage sampling system, etc.) can be continuously ‘listening’ (e.g.,operating in a scanning mode, receiving mode, coupled to or include aradio operating in a suitable mode, etc.) for information from theimaging modality, can receive information in response to prompting of ahealthcare facility worker, in response to a particular scan type beinginitiated (e.g., in response to a head CTA scan being initiated), or inresponse to any other suitable trigger.

Image data is preferably received at the router (e.g., directly,indirectly, etc.) from the imaging modality (e.g., scanner) at which thedata was generated. Additionally or alternatively, image data or anyother data can be received from any computing system associated with thehealthcare facility's PACS server, any DICOM-compatible devices such asa DICOM router, or any other suitable computing system. The image datais preferably in the DICOM format but can additionally or alternativelyinclude any other data format.

In addition to or alternative to image data, the data can include blooddata, electronic medical record (EMR) data, unstructured EMR data,health level 7 (HL7) data, HL7 messages, clinical notes, or any othersuitable data related to a patient's medical state, condition, ormedical history.

The data preferably includes a set of one or more instances (e.g.,images), which can be unorganized, organized (e.g., into a series, intoa study, a sequential set of instances based on instance creation time,acquisition time, image position, instance number, unique identification(UID), other acquisition parameters or metadata tags, anatomical featureor location within body, etc.), complete, incomplete, randomly arranged,or otherwise arranged.

Each instance preferably includes (e.g., is tagged with) a set ofmetadata associated with the image dataset, such as, but not limited to:one or more patient identifiers (e.g., name, identification number, UID,etc.), patient demographic information (e.g., age, race, sex, etc.),reason for presentation (e.g. presenting symptoms, medical severityscore, etc.), patient history (e.g., prior scans, prior diagnosis, priormedical encounters, etc.), medical record (e.g. history of presentillness, past medical history, allergies, medications, family history,social history, etc.), scan information, scan time, scan type (e.g.,anatomical region being scanned, scanning modality, scanner identifier,etc.), number of images in scan, parameters related to scan acquisition(e.g., timestamps, dosage, gurney position, scanning protocol, contrastbolus protocol, etc.), image characteristics (e.g., slice thickness,instance number and positions, pixel spacing, total number of slices,etc.), or any other suitable information.

In some variations, any or all of the data (e.g., image data) is taggedwith metadata associated with the standard DICOM protocol.

In some variations, one or more tags is generated and/or applied to thedata after the data is generated at an imaging modality. In someexamples, the tag is an identifier associated with the 1^(st) point ofcare (e.g., 1^(st) point of care location, imaging modality identifier,etc.), which can be retrieved by a 2^(nd) point of care in order tolocate the patient (e.g., to enable a quick transfer of the patient, toinform a specialist of who to contact or where to reach the patient,etc.).

Additionally or alternatively, image data can be received withoutassociated metadata (e.g., metadata identified later in the method,dataset privately tagged later with metadata later in the method, etc.)

In a first set of variations for detecting ICH, metadata of images arechecked for metadata inclusion criteria, including/indicating any or allof: a CT scan of the head, an axial series, a slice thickness within asupported range, the absence of missing slices, aligned instance numbersand positions, patient age above a minimum threshold (e.g., between 0and 10, between 10 and 20, between 20 and 30, above 30, etc.),consistent pixel spacing, a total number of slices within apredetermined range (e.g., between 18 and 25), and/or any other suitablemetadata inclusion criteria.

Data can be received (e.g., at the router) through a wired connection(e.g., local area network (LAN) connection), wireless connection, orthrough any combination of connections and information pathways.

In a first variation (e.g., for a patient presenting with symptoms of anICH), S205 includes receiving an NCCT scan of a patient's head. In apreferred specific example of this variation, the NCCT scan includes anaxial series of images (equivalently referred to herein as instancesand/or slices), each of the images corresponding to an axial thicknessbetween 2.5 and 5 millimeters, wherein the axial series includes nomissing slices and wherein the slices are properly ordered (e.g.,instance numbers and positions aligned).

4.2 Method—Transmitting Data to a Remote Computing System S208

The method can include transmitting data to remote computing system(e.g., remote server, PACS server, etc.) S208, which functions to enableremote processing of the data, robust process, or fast processing (e.g.,faster than analysis done in clinical workflow, faster than done in astandard radiology workflow, processing less than 20 minutes, less than10 minutes, less than 7 minutes, etc.) of the dataset.

The data (e.g., image data, image data and metadata, etc.) is preferablytransmitted to a remote computing system from a router (e.g., virtualmachine operating on a scanner) connected to a computing system (e.g.,scanner, workstation, PACS server, etc.) associated with a healthcarefacility, further preferably where the patient first presents (e.g.,1^(st) point of care), but can additionally or alternatively betransmitted from any healthcare facility, computing system, or storagesite (e.g., database).

Each instance (e.g., image) of the dataset (e.g., image dataset) ispreferably sent individually as it is generated at an imaging modalityand/or received at a router, but additionally or alternatively, multipleinstances can be sent together after a predetermined set (e.g., series,study, etc.) has been generated, after a predetermined interval of timehas passed (e.g., instances sent every 10 seconds), upon the promptingof a medical professional, or at any other suitable time. Furtheradditionally or alternatively, the order in which instances are sent toa remote computing system can depend one or more properties of thoseinstances (e.g., metadata). S208 can be performed a single time ormultiple times (e.g., after each instance is generated).

S208 can include transmitting all of the dataset (e.g., image datasetand metadata), a portion of the dataset (e.g., only image dataset,subset of image dataset and metadata, etc.), or any other information oradditional information (e.g., supplementary information such assupplementary user information).

The data is preferably transmitted through a secure channel, furtherpreferably through a channel providing error correction (e.g., overTCP/IP stack of 1^(st) point of care), but can alternatively be sentthrough any suitable channel.

S208 can include any number of suitable sub-steps performed prior to orduring the transmitting of data to the remote computing system. Thesesub-steps can include any or all of: encrypting any or all of thedataset (e.g., patient information) prior to transmitting to the remotecomputing system, removing information (e.g., sensitive information),supplementing the dataset with additional information (e.g.,supplementary patient information, supplemental series of a study,etc.), compressing any or all of the dataset, or performing any othersuitable process.

4.3 Method—Preparing a Data Packet for Analysis S210

The method 200 preferably includes preparing a data packet S210 foranalysis (e.g., pre-processing), which can function to pre-process anyor all of the data packet, eliminate an irrelevant (e.g., incorrectlylabeled, irrelevant anatomical region, etc.) or unsuitable data packetfrom further analysis, or perform any other suitable function.

S210 can optionally include one or more registration steps (e.g., imageregistration steps), which function to align, scale, calibrate, orotherwise adjust the set of images. Alternatively, the images canproceed to future processes without being registered. The registrationstep can be performed through registering any or all of the set ofimages either in their raw form or processed form (e.g., soft matterextracted from set of instances) to a reference set of images (e.g.,reference series); alternatively, the registration step can be performedin absence of comparing the present set of images with a reference setof images. The registration is preferably performed in response to adata packet being filtered through a set of exclusion criteria, but canadditionally or alternatively be performed prior to or in absence of thefiltering, multiple times throughout the method, or at any othersuitable time.

The registration step can be intensity-based, feature-based, orotherwise configured, and can include any or all of: point-mapping,feature-mapping, or any other suitable process. Additionally oralternatively, it can be based on a deep neural network. The referenceset of images is preferably determined from a training set but canalternatively be determined from any suitable dataset,computer-generated, or otherwise determined. The reference series can beselected for any or all of orientation, size, three-dimensionalpositioning, clarity, contrast, or any other feature. In one variation,a references series is selected from a training set, wherein thereference series is selected based on a feature parameter (e.g., largestfeature size such as largest skull, smallest feature size, etc.) and/ora degree of alignment (e.g., maximal alignment, alignment above apredetermined threshold, etc.). Additionally or alternatively, any othercriteria can be used to determine the reference series, the referenceseries can be randomly selected, formed from aggregating multiple setsof images (e.g., multiple patient series), or determined in any othersuitable way. In some variations, the registration is performed with oneor more particular software packages (e.g., SimpleElastix). In aspecific example, the registration is performed through affineregistration (e.g., in SimpleElastix) with a set of predetermined (e.g.,default) parameters and iterations (e.g., 4096 iterations, between 3000and 5000 iterations, above 1000 iterations, above 100 iterations, etc.)in each registration step. In another example, the affine transformationis computed using a deep neural network.

Preprocessing the images preferably includes resizing the images (e.g.,through downsampling, lossless compression, lossy compression, etc.),which functions to compress the images and can subsequently enableand/or shorten the time of transfer of data between points of the system(e.g., from a virtual machine to a remote computing system, from aremote computing system to a mobile device and/or workstation, etc.).Additionally, in variations involving trained deep learning models,resizing the images can function to enable fast training convergencewhile maintaining high resolution. Preprocessing the images can furtherinclude straightening out the images (e.g., rotating a portion of theimage, translating a portion of the image, etc.). In a first variation,registering a set of brain slices (e.g., from an NCCT) includes one orboth of straightening out the brain region and downsampling each image(e.g., from 512×512 to 96×96 or 256×256).

S210 can include windowing (equivalently referred to herein as clipping)the image data, which can function to increase the image contrast of apredetermined (e.g., desired) range (window) of pixel/voxel values(e.g., Hounsfield units), eliminate irrelevant information (e.g.,information not corresponding to regions of interest/potential userconditions) from further processing, and/or perform any other suitablefunction. The threshold values (e.g., HU values) determining the windowrange can optionally be determined based on any or all of: the type ofscan (e.g., contrast, non-contrast, CT, NCCT, MRI, etc.), the bodyregion scanned (e.g., head), suspected condition (e.g., type of brainhemorrhage, ICH, LVO, etc.), patient demographics and/or metadata (e.g.,age, gender, etc.), and/or any other suitable information. Additionallyor alternatively, the threshold values can be constant for all images.

In preferred variations (e.g., for detecting brain conditions), therange of HU values that are retained for processing ranges from aHounsfield unit just below that corresponding to soft matter to aHounsfield unit just above that corresponding to bone. Additionally oralternatively, a window can be shifted with respect to this (e.g.,anything below bone), narrowed with respect to this (e.g., onlyencompassing brain tissue), broadened with respect to this, or otherwiseselected.

In specific examples, the information retained has HU values based on awindow level of 1000 and a window width of 2000 (e.g., HU values between0 and 2000).

S210 can further include an effective and/or actual removal (e.g.,assignment of a pixel value of zero, actual removal through cropping,etc.) of one or more regions of the image, which is preferably performedafter and based on a windowing process (e.g., as described above), butcan additionally or alternatively be performed prior to a windowingprocess, in absence of a windowing process, in a later process (e.g.,after a segmentation process), or otherwise performed. In one variation,a set of “white” regions (e.g., regions having a pixel/voxel value of255, regions having a pixel/voxel value above a predetermined threshold,etc.) are assigned a pixel/voxel value of zero, which—in combinationwith a windowing process as described above—can function to result inthe extraction of only soft matter (e.g., brain matter, blood, cerebralfluid, etc.); this can equivalently be referred to herein as skullstripping. Additionally or alternatively, any other regions can beeffectively and/or actually removed in any suitable way (e.g., throughphotomasks, dilation, erosion, etc.).

S210 can further include the normalization of any or all of the imagedata, which can function to enable the comparison of multiple instanceswith each other, the comparison of one series with a different series(e.g., the series of a previously-taken brain scan, the series of onepatient with a reference series, the series of a first patient that thatof a second patient, etc.), enable faster training convergence of deeplearning models in processing, and/or perform any other suitablefunction. In one variation, the set of voxels in the image data arenormalized such that the voxels have a predetermined mean Hounsfieldunit value (e.g., 24.5 HU, less than 24.5 HU, greater than 24.5 HU,etc.) and a predetermined standard deviation Hounsfield unit value(e.g., 39.5 HU, less than 39.5 HU, greater than 39.5 HU, etc.).Additionally or alternatively, the image data can be normalized in anyother suitable way with respect to any suitable parameter. In specificexamples, the image data is normalized by dividing by a fixed number(e.g., between 200 and 300, 250, 255, 260, etc.).

S210 can optionally include organizing the set of instances, preferablyinto a series, but additionally or alternatively into a study, or anyother suitable grouping of images. The organization is preferablyperformed in response to generating a set of instances (e.g., at animaging modality), but can additionally or alternatively be performed inresponse to receiving a set of instances at a location (e.g., router,remote computing system, server such as a PACS server, etc.), at therequest of an individual (e.g., healthcare worker), in response to atrigger, in response to any other pre-processing step, or at any othersuitable time.

In some variations, the method includes excluding a data packet (e.g.,set of instances) from further processing if one or more of a set ofmetadata are not satisfied, such as, but not limited to, the metadatalisted above.

In a specific example, a bone mask is determined and defined as a set ofvoxels having an HU value above a predetermined threshold (e.g., 750 HU,700 HU, 800 HU, between 600 HU and 900 HU, etc.). The bone mask is thendilated with a series of kernels of increasing size until it completelyencloses a set of voxels of low HU values (e.g., less than thepredetermined threshold), thereby defining a soft matter mask. The softmatter mask is dilated to compensate for the dilation of the bone mask.If the process of defining the soft matter mask fails, this can indicatethat the skull has undergone a craniotomy, which in some cases can beused in determining a diagnosis, informing a contact or second point ofcare, or in any other point in the method. Once the soft matter mask isdilated, the mask can then be applied to the set of instances (e.g.,organized set of instances, series, etc.), and the HU value of voxelsoutside of the mask is set to zero.

In a first set of variations, S210 includes resizing/downsampling theset of images (e.g., from 512×512 to 256×256); removing irrelevantinformation from the set of images based on Hounsfield Unit windowingprocess (e.g., clipping voxels corresponding to HU value below 1000 andabove 2000); and normalizing the HU values of the image set voxels(e.g., by dividing by a fixed number).

4.4 Method—Checking for a Suspected Condition Associated with the DataPacket S220

The method 200 includes checking for a suspected condition andoptionally one or more parameters of the suspected condition (e.g., asshown in FIG. 9 ) associated with the data packet S220, which functionsto inform the remaining processes of the method 200. Additionally oralternatively, S220 can function to reduce the time to transfer apatient to a second point of care, halt progression of the condition, orperform any other suitable function. S220 is preferably fully performedat a remote computing system (e.g., remote server, cloud-based server,etc.), further preferably a remote computing system having a graphicsprocessing unit (GPU), but can additionally or alternatively bepartially performed at any suitable remote computing system, bepartially or fully performed at a local computing system (e.g.,workstation), server (e.g., PACS server), at a processor of a userdevice, or at any other system. S220 is preferably partially or fullyperformed using software including one or more algorithms, furtherpreferably one or more multi-step algorithms containing steps that areeither trained (e.g., trained through machine learning, trained throughdeep learning, continuously trained, etc.) or non-trained (e.g.,rule-based image processing algorithms or heuristics). Additionally oralternatively, any software can be implemented.

In preferred variations, S220 includes processing an input produced inS210 with one or more deep learning models, preferably deep CNNs, butadditionally or alternatively any other suitable deep learning models.The deep learning models are preferably trained on training dataincluding at least images corresponding to (e.g., containing, diagnosedas, testing positive for, etc.) the condition being tested for in themethod 200 and images absent of (e.g., testing negative for) thecondition. In preferred variations, the deep learning models areadditionally iteratively trained based on the results of an earlierversion of the model(s) (e.g., through one or more boosting algorithms).In specific examples, the results of a first set of models whichindicated the presence of a condition (tested positive) and were laterfound to not contain the condition (false positives) are used astraining data to minimize the occurrence of false positivedeterminations. In specific examples, the false positive inputs arelabeled (e.g., as mistakenly labeled, as a common mistake, as negative,as a false positive, etc.) and trained on these more difficult cases.

The method can optionally additionally or alternatively includeproducing augmented data for training one or more models, which caninclude any or all of: random rotations (e.g., 2D rotations) of images(e.g., rotations between 45 degrees counterclockwise and 45 degreesclockwise), random shifts (e.g., in the 2D plane) of images (e.g.,shifts of 25% or less in a horizontal axis, shifts of 16% or less in ahorizontal axis, shifts of less than 15% in a vertical axis, shifts ofless than 12% in a vertical axis, etc.), 2D scale changes (e.g., in therange of 0.8×-1.2×), horizontal flips, the addition of image noise,and/or any other suitable modifications of images.

S220 can optionally include producing and/or receiving a second imageset, such as a modified version (e.g., mirror image horizontally, mirrorimage vertically, rotated, translated, etc.) of the image set producedin S210, which can function to increase the accuracy and/or likelihoodof detecting a patient condition. In some variations, such as those inwhich a symmetric anatomical region (e.g., brain) is being analyzed,this can improve the likelihood that the patient condition is accuratelydetected (e.g., as the present images could most closely relate to abrain condition occurring in an opposite brain hemisphere in thetraining data). The results of the first and second set of images can beaveraged, combined in a weighted fashion, used as inputs for analgorithm or model, compared and the highest value ultimately selectedfor use in subsequent processes of the method, and/or otherwise used. Inspecific examples, the pre-processed brain images produced in S210 and ahorizontal flip of these images are fed through a feed-forward deepconvolutional neural network (CNN) (e.g., trained to segmenthyper-attenuated regions consistent with ICH), wherein the predictionsof the scan and its horizontal flip are averaged.

S220 preferably includes identifying (e.g., locate, isolate, measure,quantify, etc.) and segmenting an affected brain region (e.g., brainregion saturated with blood, blood region, etc.) within one or more ofthe set of instances, thereby indicating a hemorrhagic brain condition(e.g., ICH) or at least a potential hemorrhagic brain condition. Inpreferred variations, the hemorrhagic brain condition able to bedetected includes at least an intracerebral hemorrhage (e.g., anintraparenchymal hemorrhage, a subarachnoid hemorrhage, etc.); anintraventricular hemorrhage; an extradural hemorrhage; a subduralhemorrhage; any combination of hemorrhages; and/or any other suitablehemorrhage. Additionally or alternatively, any other brain conditions(e.g., vessel occlusion) or other health condition (e.g., cardiaccondition, lung condition, etc.) can be detected. This can be performedthrough any number of computer vision techniques, such as objectrecognition, object identification, object detection, or any other formof image analysis.

In some variations, identifying an affected brain region (e.g., bloodregion) is performed at least partially through image segmentation,wherein the segmentation includes any or all of: thresholding,clustering methods, dual clustering methods, compression-based methods,histogram-based methods, region-growing methods, partial differentialequation-based methods, variational methods, graph partitioning methods,watershed transformations, model based segmentation, multi-scalesegmentation, semi-automatic segmentation, trainable segmentation, orany suitable form of segmentation. The method can additionally oralternatively include any number of segmentation post-processing steps,such as thresholding, connectivity analyses, or any other processing.The segmentation is preferably performed with a convolutional neuralnetwork (CNN), further preferably feed-forward deep CNN (e.g., usingthree-dimensional convolutions, two-dimensional convolutions, etc.), butcan additionally or alternatively be performed using any suitablealgorithm or process.

The segmentation is preferably performed for an input including a set ofpre-processed image slices (e.g., according to S210 as described above),further preferably consecutive pre-processed image slices. Additionallyor alternatively, a segmentation of un-processed (e.g., raw), unordered,and/or a set of slices missing intermediate slices, can be performed.

The segmentation preferably includes assigning a probability score(e.g., between 0 and 1) to each base unit (e.g., pixel, voxel, etc.) ofthe input, which indicates a likelihood that the base unit represents apart of the brain condition (e.g., ICH) or set of brain conditions beingtested for. A segmentation region (e.g., initial segmentation region,final segmentation region, etc.) is then formed from the base unitshaving a probability score above a predetermined threshold.

In one variation, the segmentation of a set of NCCT scan slicesfunctions to segment hyper-attenuated brain regions which are consistentwith a hemorrhage (e.g., ICH). In a specific example, a 3D array isformed from the set of scan slices, the 3D array containing a set ofprobability values between 0 and 1 for each of the set of voxels in a 3Drepresentation of the scan slices, the probability values correspondingto a likelihood that each voxel corresponds to a hemorrhage/hemorrhagedregion (equivalently referred to herein as a hyperdensity) of the brain(e.g., region of brain tissue containing blood). A set of voxels (e.g.,contiguous voxels, connected voxels, bordering voxels, etc.), preferablyadjacent (e.g., contiguous) voxels (but alternatively non-adjacent orpartially adjacent voxels), having a probability of at least apredetermined threshold (e.g., 0.5, 0.6, 0.7, 0.8, 0.9, etc.) representsa region suspected of having a hemorrhage. The probabilistic output ofthe network is converted into a binary mask defined as all voxels havinga probability greater than this threshold (e.g., 0.5, 0.7, between 0.4and 0.8, etc.), which forms the segmentation.

The probability threshold can optionally be dependent on any number offeatures of the segmentation and/or images, such as any or all of: thelocation of the segmented region (e.g., intraparenchymal,intraventricular, epidural, subdural, subarachnoid, etc.), the suspectedcondition (e.g., and a severity of the condition being tested for),metadata, and/or any other suitable features. Alternatively, theprobability threshold can be constant.

In a set of variations, a voxel probabilistic output is converted to abinary mask defined to be all voxels having a probability greater than athreshold, preferably a threshold that provides a favorable compromisebetween sensitivity and specificity. If specific examples, the thresholdis equal to 50%. Additionally or alternatively, the threshold can begreater than 50% for higher specificity (e.g., between 50% and 60%, 60%,70%, 80%, 90%, between 50% and 100%, etc.), less than 50% for highersensitivity (e.g., between 40% and 50%, 40%, 30%, 20%, 10%, greater than0%, etc.), and/or any other percentage for any suitable objective.

Additionally or alternatively, probabilities from multiple voxels can beaggregated (e.g., averaged) and compared with a threshold, a minimumnumber of high probabilities can need to be reached, and/or anyprobabilities can be examined in any suitable ways.

S220 further preferably includes evaluating one or more exclusioncriteria in the segmentation and/or any other part of the image data,which can function to verify that the image data is relevant forevaluation in the rest of the method, save time and/or resources byeliminating irrelevant scans, check for a particular set of falsepositives (e.g., artifacts, eliminate one or more categories of falsepositives while still maintaining an overall high percentage of falsepositives, eliminate one or more categories of false positives whilestill maintaining an overall high percentage of false negatives,minimizing a number of false positives, etc.), route a set of instancescorresponding to one or more exclusion criteria to another workflow in ahealthcare facility, or perform any other suitable function. In somevariations, for instance, a set of exclusion criteria are evaluated,which function to keep “close call” false positives (e.g., questionablepathologies, affected brain tissue, etc.) while eliminating falsepositives caused by non-physiological events (e.g., metal artifact, poorimage quality, etc.). Additionally or alternatively, exclusion criteriaare evaluated to minimize an overall number of false positives, therebyminimizing, for instance, unnecessary interruptions to specialistsand/or any other number of recipients (e.g., clinical trial principalinvestigators). Alternatively, the method can partially or fully processall sets of instances. The exclusion criteria can be: learned (e.g.,from false positives and/or false negatives; labeled training data;training such as iterative training of one or more deep learning modelswith images corresponding to false positives; etc.); manually determined(e.g., be a set of rules, a decision tree, a heuristic, etc.); or beotherwise determined.

Evaluating exclusion criteria preferably includes checking for any orall of: the presence of an artifact (e.g., metallic artifact, aneurysmclip, etc.) in one or more of the set of images, improper timing atwhich the set of images were taken at an imaging modality (e.g.,premature timing, improper timing of a bolus, etc.), one or moreincomplete regions (e.g., features, anatomical features, etc.) in theset of images (e.g., incomplete skull, incomplete vessel, incompletesoft matter region, etc.), an incorrect scan type or body part (e.g.,non-head CT scan, non-contrast CT scan, etc.), poor image quality (e.g.,blurry images, low contrast, etc.), movement of the patient during thescan (e.g., manifesting as bright streaks in one or more images), or anyother suitable exclusion criteria.

In one variation, a set of images are evaluated to determine if anartifact is present, wherein the set of images is excluded from furthersteps in the method if an artifact is found. In a specific example, themethod includes inspecting the HU values of voxels in a soft mattermask, wherein voxels having a value above a predetermined threshold(e.g., 3000 HU, between 2000 and 4000 HU, etc.) are determined to be ametallic artifact (e.g., aneurysm clip).

Evaluating exclusion criteria can include a first subroutine, whichfunctions to evaluate the set of instances on a per-slice basis, whereineach slice having been processed through a segmentation process (e.g.,as described above, producing a set of regions having the same orsimilar probability values, etc.) is divided into connected components(e.g., regions made up of contiguous voxels having the same or a similarprobability value, regions made up of contiguous voxels having aprobability above a predetermined threshold, etc.). The connectedcomponents can then be eliminated from further processing (e.g.,discarded) based on a set of thresholds in any number of a set ofcategories.

The set of categories preferably includes a size category, whereinconnected components (e.g., segmentations) are evaluated based on any orall of: area (e.g., number of pixels), volume (e.g., volume in mL,number of voxels etc.), one or more characteristic dimensions (e.g.,maximum dimension, minimum dimension, length, width, maximum length,maximum width, thickness, radius, diameter, etc.), or any other suitablesize category. The size can be compared with a threshold, wherein if thesize is below the threshold (e.g., indicating an artifact, indicatingnoise, etc.), the component is eliminated from further processing and/orconsideration. Additionally or alternatively, if the size is above athreshold (e.g., indicating an image quality issue, indicating an ICHtoo severe for intervention, etc.), the component can be eliminated fromfurther processing and/or consideration; if the size is within a rangeof thresholds, the component can be eliminated from further processingand/or consideration; if the size is outside a range of thresholds, thecomponent can be eliminated from further processing and/orconsideration; or the component can be otherwise evaluated and/orfurther processed.

In a first variation (e.g., ICH), a size of the segmented region iscompared with a minimum volume threshold, wherein if the segmentedregion size is below the minimum volume threshold, the segmented regionis eliminated from further consideration of the condition, and whereinif the segmented region size is above the minimum volume threshold, thesegmented region continues to further processing and/or results in thedetermination of the suspected condition. In specific examples, theminimum volume threshold is 0.1 mL. Additionally or alternatively, thevolume threshold can be greater than 0.1 mL (e.g., 0.2 mL, 0.3 mL, 0.4mL, 0.5 mL, between 0.5 and 1 mL, 1 mL or greater, etc.), less than 0.1mL (e.g., 0.09 mL, 0.08 mL, 0.07 mL, 0.06 mL, 0.05 mL, between 0 and0.05 mL, etc.), and/or have any other suitable value. Furtheradditionally or alternatively, the size threshold can include a 2D sizethreshold (e.g., per slice), 1D size threshold (e.g., largest length ofsegmentation in a slice, largest width of segmentation in a slice,thickness, etc.), and/or any other suitable thresholds.

In a second variation, additional or alternative to the first, the sizecategory can be configured to determine if a suspected blood region isinstead a movement of the patient while in the scanner, which canmanifest itself in bright, “blood-like” streaks in the image. If it'sdetermined that the size (e.g., area, volume, etc.) of a suspected bleedis above a predetermined threshold, yet only shows up on a number ofimages below a predetermined threshold (e.g., only one slice, less than3 slices, less than 10 slices, etc.), then it can likely instead beattributed to the patient moving during the scan.

The set of categories can optionally include a location category,wherein connected components (e.g., segmentations) are evaluated basedon their proximity to another component, such as another anatomicalcomponent (e.g., bone, skull, etc.), a particular brain region or brainfeature (e.g., particular sulcus, lobe, covering, etc.), an imagefeature (e.g., an image edge, etc.), or any other component or generallocation. In the event that the relative location (e.g., distance,proximity, etc.) is below a threshold (indicating that the component istoo close), the component can be discarded. In some variations involvingbrain hemorrhage detection, this can be used to categorize a suspectedbrain hemorrhage as being one of: an intracerebral hemorrhage (e.g., anintraparenchymal hemorrhage, a subarachnoid hemorrhage, etc.); anintraventricular hemorrhage; an extradural hemorrhage; a subduralhemorrhage; any combination of hemorrhages.

Additionally or alternatively, any other categories and associatedthresholds can serve as exclusion category in the first subroutine.

In one variation, the first subroutine includes dividing each slice ofthe segmentation mask into connected components, wherein connectedcomponents smaller than a predetermined volume (e.g., 0.4 milliliters[mL], less than 0.4 mL, greater than 0.4 mL, etc.) and less than apredetermined number of voxels away from a bone voxel (e.g., less than 3voxels away; less than a threshold greater than 3 voxels away; less thana threshold less than 3 voxels away; etc.) are eliminated from futureanalysis (e.g., discarded).

Evaluating exclusion criteria can further include a performing a secondsubroutine, which functions to evaluate the set of instances on avolumetric basis, wherein the entire segmentation mask is divided into3D connected components. The second subroutine is preferably performedafter the first subroutine has been performed (e.g., after a first setof components have been eliminated, after a set of slices has beeneliminated, etc.). Additionally or alternatively, the second subroutinecan be performed in parallel with the first subroutine, prior to thefirst subroutine, in absence of the first subroutine, and/or at anysuitable time. Further additionally or alternatively, the method caninclude any other subroutine(s).

The second subroutine can evaluate the components based on similarand/or the same categories as described for the first subroutine;additionally or alternatively, any other suitable categories andassociated thresholds can be evaluated. These can include any number ofsize categories, such as, but not limited to: volume, thickness (e.g.,anatomical thickness, number of slices, number of consecutive slices, athickness extracted from the number of slices, etc.), a characteristicdimension (e.g., width, length, diameter, etc.), curvature, or any othersuitable size parameter. These can further include any number oflocation categories (e.g., proximity to another anatomical region,location as described above), or any other suitable categories

In a first variation of the second subroutine, a segmentation mask isdivided into connected components in 3D, wherein the thickness of aconnected component is defined as the total number of consecutive slicescontaining voxels belonging to that connected component. Components witha thickness below a predetermined threshold (e.g., thickness of 1,thickness below 2, thickness below 3, thickness below 5, thickness below10, etc.) are eliminated from further analysis.

Additionally or alternatively, any other subroutine(s) can be performed.In variations involving multiple subroutines (e.g., to analyze a set ofimages in multiple different dimensions, to analyze a set of imagesbased on multiple different categories, to analyze a set of images basedon increasingly more stringent criteria, to analyze a set of imagesbased on increasingly less stringent criteria, to analyze a set ofimages based on multiple different procedures, etc.), the subroutinescan be performed in series (e.g., using the output of a previoussubroutine as the input of the present subroutine, using the same inputas the previous subroutine, etc.), in parallel, or in a combination ofseries and parallel. In preferred variations, each subsequent subroutinebuilds upon the previous (e.g., uses the results of the previoussubroutine to check for further exclusion criteria). In alternativevariations, a set of subroutines are performed independently of eachother and the results combined (e.g., consolidated, weighted, etc.) todetermine a final output.

Based on the outcome of the segmentation and/or evaluation of exclusioncriteria (or alternatively the original segmentation), one or moredeterminations of the patient condition can be made and/or ruled out.The condition typically refers to a predicted (e.g., hypothesized,assumed, having a probability above a predetermined threshold, etc.)patient condition or diagnosis (e.g., ICH, LVO, aneurysm, stroke, etc.)but can additionally or alternatively include a severity (e.g., based ona predetermined severity scale), an urgency, or any othercharacteristic.

In a first set of variations, if the connected component, which isformed from the connecting of voxels having a probability above apredetermined threshold (e.g., 50%), is above a predetermined sizethreshold (e.g., volume threshold above 0.1 mL), the condition isdetermined to be suspected.

In a second set of variations, if the connected component (e.g., afterperforming the subroutines) contains at least one voxel marked as acritical voxel (e.g., corresponding to ICH, corresponding to anotherbrain condition, etc.), it is determined that the patient has thepotential condition (e.g., ICH). Additionally or alternatively, anysuitable threshold(s) can be used (e.g., more than one voxel marked ascritical, between 1 and 20 voxels marked as critical, less than 20voxels marked as critical, etc.).

The determination preferably triggers one or more outcomes (e.g., asdescribed below), but can additionally or alternatively prompt furtheranalysis, such as the determination of one or more associatedparameters. In variations of ICH, for instance, these can include any orall of: an amount of compromised brain matter (e.g., hyperdense,containing blood of at least a predetermined threshold density, regionproximal to an occluded vessel, etc.), an amount of uncompromised brainmatter, the particular affected region (e.g., brain territory, brainlobe, etc.), the function of the affected region (e.g., motor control,memory, emotion, etc.), a trajectory of the affected region (e.g., rateof spreading, blood flow rate, time until critical, time until fatal,etc.), or any other suitable parameter.

Additionally or alternatively, S220 can include any other processesperformed in any suitable order.

4.5 Method—In an Event that the Suspected Condition is Detected,Determining a Recipient Based on the Suspected Condition S230

The method includes in an event that the suspected condition isdetected, determining a recipient based on the suspected condition S230,which functions to facilitate the treatment (e.g., triage, acceptanceinto a clinical trial, etc.) of the patient.

S230 can additionally, alternatively, and/or equivalently includedetermining a treatment option, preferably in the event that a conditionis detected (e.g., based on a comparison with a threshold, based on abinary presence, etc.) but can additionally or alternatively determine atreatment option when a condition is not detected, when an analysis isinconclusive, or in any suitable scenario. S230 can function to matchthe patient with a specialist, initiate the transfer of a patient to a2^(nd) point of care (e.g., specialist facility), initiate the transferof a specialist to a 1^(st) point of care, initiate treatment of apatient (e.g., surgery, stent placement, mechanical thrombectomy, etc.)within the 1^(st) point of care, initiate the matching of a patient to aclinical trial, or perform any other suitable function. In somevariations, the treatment option is a 2^(nd) point of care, wherein itis determined (e.g., suggested, assigned, etc.) that the patient shouldbe treated at the 2^(nd) point of care. Additionally or alternatively,the treatment option can be a procedure (e.g., surgical procedure,surgical clipping, mechanical thrombectomy, placement of an aneurysmcoil, placement of a stent, retrieval of a thrombus, stereotacticradiosurgery, etc.), treatment (e.g., tissue plasminogen activator(TPA), pain killer, blood thinner, etc.), recovery plan (e.g., physicaltherapy, speech therapy, etc.), or any other suitable treatment.

The treatment is preferably determined based on a parameter determinedfrom the data packet (e.g., binary presence of a condition, comparisonof a parameter with a threshold, etc.), but can additionally oralternatively be determined based on additional data, such as patientinformation (e.g., demographic information, patient history, patienttreatment preferences, etc.), input from one or more individuals (e.g.,power of attorney, attending physician, emergency physician, etc.), aconsensus reached by multiple recipients of a notification (e.g.,majority of members of a care team, all members of a care team, etc.),or any other suitable information.

S230 is preferably at least partially performed with software operatingat the remote computing system (e.g., remote server) but canadditionally or alternatively be performed at a remote computing systemseparate from a previous remote computing system, a local computingsystem (e.g., local server, virtual machine coupled to healthcarefacility server, computing system connected to a PACS server), or at anyother location.

S230 is preferably performed after a patient condition has beendetermined during the method 200. Additionally or alternatively, S230can be performed after a patient condition has been determined in analternative workflow (e.g., at the 1^(st) point of care, at aradiologist workstation during a standard radiology workflow, in thecase of a false negative, etc.), prior to or absent the determination ofa patient condition (e.g., based on an input from a healthcare worker atthe remote computing system, when patient is admitted to 1^(st) point ofcare, etc.), multiple times throughout the method (e.g., after a firsttreatment option fails, after a first specialist is unresponsive, suchas after a threshold amount of time, such as 30 seconds, 1 minute, 2minutes, etc.), or at any other time during the method.

S230 preferably determines a treatment option with a lookup tablelocated in a database accessible at remote computing system (e.g.,cloud-computing system). Additionally or alternatively, a lookup tablecan be stored at a healthcare facility computing system (e.g., PACSserver), in storage at a user device, or at any other location.

In other variations, the treatment option can be determined based on oneor more algorithms (e.g., predictive algorithm, trained algorithm,etc.), one or more individuals (e.g., specialist, care team, clinicaltrial coordinator, etc.), a decision support tool, a decision tree, aset of mappings, a model (e.g., deep learning model), or through anyother process or tool.

The lookup table preferably correlates a 2^(nd) point-of-care (e.g.,healthcare facility, hub, physician, specialist, neuro-interventionist,etc.), further preferably a specialist or contact (e.g., administrativeworker, emergency room physician, etc.), with a patient condition (e.g.,presence of an ICH, presence of an LVO, presence of a pathology,severity, etc.), but can additionally or alternatively correlate anytreatment option with the patient condition. The lookup table canfurther additionally or alternatively correlate a treatment option withsupplementary information (e.g., patient history, demographicinformation, heuristic information, etc.).

The contact (e.g., healthcare provider, neuro-interventional specialist,principal investigator, stroke care team member, principal investigator,clinical trial enrollment committee, etc.) is preferably a healthcareworker, but can additionally or alternatively be any individualassociated with the treatment of the patient and/or be associated withany healthcare facility (e.g., prior healthcare facility of patient,current healthcare facility, recommended healthcare facility) related tothe patient. The contact is further preferably a specialist (e.g.,neuro-interventional specialist, neurosurgeon, neurovascular surgeon,general surgeon, cardiac specialist, etc.) but can additionally oralternatively include an administrative worker associated with aspecialist, multiple points of contact (e.g., ranked order, group,etc.), or any other suitable individual or group of individuals. Thecontact is preferably associated with a hub facility, wherein the hubfacility is determined as an option for second point of care, but canadditionally or alternatively be associated with a spoke facility (e.g.,current facility, future facility option, etc.), an individual with arelation to the patient (e.g., family member, employer, friend,acquaintance, emergency contact, etc.), or any other suitable individualor entity (e.g., employer, insurance company, etc.). Additionally oralternatively, the contact can be an individual associated with aclinical trial (e.g., principal investigator at a 1^(st) point of care,principal investigator at a 2^(nd) point of care, approval/enrollmentcommittee to approve a patient for a clinical trial, etc.), and/or anyother suitable individual.

The lookup table is preferably determined based on multiple types ofinformation, such as, but not limited to: location information (e.g.,location of a 1^(st) point of care, location of a 2^(nd) point of care,distance between points of care, etc.), temporal information (e.g., timeof transit between points of care, time passed since patient presentedat 1^(st) point of care, etc.), features of condition (e.g., size ofocclusion, severity of condition, etc.), patient demographics (e.g.,age, general health, history, etc.), specialist information (e.g.,schedule, on-call times, historic response time, skill level, years ofexperience, specialty procedures, historic success or procedures, etc.),healthcare facility information (e.g., current number of patients,available beds, available machines, etc.), but can additionally oralternatively be determined based on a single type of information or inany other suitable way. Information can be actual, estimated, predicted,or otherwise determined or collected.

In some variations, the method 200 includes transmitting information(e.g., patient condition, data determined from analysis, optimal set ofinstances, series, data packet, etc.) to the computing system associatedwith the lookup table.

4.6 Method—Preparing a Data Packet for Transfer S240

The method 200 can include preparing a data packet for transfer, whichcan function to produce a compressed data packet, partially or fullyanonymize a data packet (e.g., to comply with patient privacyguidelines, to comply with Health Insurance Portability andAccountability Act (HIPAA) regulations, to comply with General DataProtection Regulation (GDRP) protocols, etc.), minimize the time totransfer a data packet, annotate one or more images, or perform anyother suitable function. Additionally or alternatively, any or all of adata packet previously described can be transferred.

The data packet is preferably transferred (e.g., once when data packetis generated, after a predetermined delay, etc.) to a contact, furtherpreferably a specialist (e.g., associated with a 2^(nd) point of care,located at the 1^(st) point of care, etc.), but can additionally oralternatively be sent to another healthcare facility worker (e.g., at1^(st) point of care, radiologist, etc.), an individual (e.g., relative,patient, etc.), a healthcare facility computing system (e.g.,workstation), a server or database (e.g., PACS server), or to any othersuitable location.

S240 preferably includes compressing a set of images (e.g., series), butcan additionally or alternatively leave the set of images uncompressed,compress a partial set of images (e.g., a subset depicting thecondition), or compress any other part of a data packet. Compressing thedata packet functions to enable the data packet to be sent to, receivedat, and viewed on a user device, such as a mobile device. Compressingthe data packet can include any or all of: removing a particular imageregion (e.g., region corresponding to air, region corresponding to hardmatter, region without contrast dye, irrelevant anatomical region,etc.), thresholding of voxel values (e.g., all values below apredetermined threshold are set to a fixed value, all values above apredetermined threshold are set to a fixed value, all values below −500HU are set to −500, all voxel values corresponding to a particularregion are set to a fixed value, all voxels corresponding to air are setto a predetermined fixed value, etc.), reducing a size of each image(e.g., scale image size by factor of 0.9, scale image size by factor of0.7, scale image size by factor of 0.5, scale image size by a factorbetween 0.1 and 0.9, reduce image size by a factor of 4, etc.), orthrough any other compression method.

In one variation, the reduction in size of a set of images can bedetermined based on one or more memory constraints of the receivingdevice (e.g., user device, mobile device, etc.).

In some variations, such as those involving a patient presenting with abrain condition (e.g., ICH, LVO), the images taken at an imagingmodality (e.g., CT scanner) are compressed by determining an approximateor exact region in each image corresponding to air (e.g., based on HUvalue, based on location, based on volume, etc.) and setting the airregion (e.g., voxels corresponding to the air region, pixelscorresponding to the air region, etc.) to have a fixed value.Additionally or alternatively, any non-critical region (e.g., bone,unaffected region, etc.) or other region can be altered (e.g., set to afixed value, removed, etc.) during the compression. In a specificexample, for instance, a set of voxels corresponding to air are set toall have a common fixed value (e.g., an upper limit value, a lower limitvalue, a value between 0 and 1, a predetermined value, etc.).

In some variations, S240 includes identifying an optimal visualizationto be transmitted (e.g., from a remote computing system) and received(e.g., at a user device), which functions to prepare an optimal outputfor a 2^(nd) point of care (e.g., specialist), reduce the time requiredto review the data packet, bring attention to the most relevant imagedata, or to effect any other suitable outcome.

In some variations, this involves a reverse registration process. In aspecific example, for instance, this is done through maximum intensityprojection (MIP), where an optimal range of instances is determinedbased on which images contain the largest percentage of the segmentedanatomical region of interest in a MIP image.

Additionally or alternatively, S240 can include removing and/or altering(e.g., encrypting) metadata or any unnecessary, private, confidential,or sensitive information from the data packet. In some variations,patient information (e.g., patient-identifiable information) is removedfrom the data packet in order to comply with regulatory guidelines. Inother variations, all metadata are extracted and removed from the datapacket.

S240 can optionally include annotating one or more images in the datapacket, which can function to draw attention to one or more features ofthe images, help a specialist or other recipient easily and efficientlyassess the images, and/or perform any other suitable functions.

Annotating the images can optionally include adding (e.g., assigning,overlaying, etc.) one or more visual indicators (e.g., labels, text,arrows, highlighted or colored regions, measurements, etc.) to one ormore images. The incorporation of the visual indicators can bedetermined based on any or all of: the suspected condition (e.g., typeof visual indicators designated for the condition based on a lookuptable), one or more thresholds (e.g., size thresholds), features of thesuspected condition/pathology (e.g., location of hemorrhage withinbrain), preferences (e.g., specialist preferences, point of carepreferences, etc.), guidelines (e.g., patient privacy guidelines), theresults of one or more deep learning models, and/or any other suitablefactors or information. In variations with visual indicators, atable/key can optionally be provided (e.g., as shown in FIG. 8 ) toexplain the visual indicators, which can include any or all of: a colorkey defining what colors correspond to; one or more parametersassociated with an indicated region or feature (e.g., volume ofhyperdensities identified in each region); one or more parametersassociated with the image(s) as a whole (e.g., total hyperdensity volumemeasured across all regions, number of regions indicated, etc.); and/orany other suitable information.

Images can additionally or alternatively be annotated with one or moremetrics, such as one or more parameters (e.g., size as described above);scores (e.g., a clinical score, a severity score, etc.); instructions(e.g., recommended intervention); and/or any other suitable information.In some variations for annotating images associated with a suspectedbrain hemorrhage (e.g., ICH), an Alberta Stroke Program Early CT Score(ASPECTS), which is a quantitative score used to assess ischemic damageand predict irreversible injury, can be calculated and annotated on oneor more images. Additionally or alternatively, any other suitable scorecan be calculated and provided to (e.g., in a message, email, etc.) arecipient.

Additionally or alternatively to being annotated on an image, any or allof the annotations can be provided in a separate notification, such as amessage, document, and/or provided in any other suitable way.

The annotations are preferably determined automatically (e.g., at aremote computing system implementing the deep learning models, at aclient application, at a mobile device executing a client application,etc.), but can additionally or alternatively be determined manually,verified manually, or otherwise determined.

In some variations of hemorrhage detection, for instance, a size of thehemorrhage (e.g., volume) has been found to be helpful indecision-making for the specialist (e.g., determining whether or not tointervene, determining a type of intervention, etc.) and is indicated tothe specialist on one or more images transmitted to him or her. Inspecific examples (e.g., as shown in FIG. 8 , the hemorrhage region isindicated by (e.g., highlighted in, outlined in, etc.) one of a set ofcolors, wherein the color indicates a type of hemorrhage (e.g.,intraparenchymal, intraventricular, epidural/subdural, subarachnoid,etc.). Additionally, a calculated volume of the region is preferablyprovided. Additionally or alternatively, any other suitable informationcan be provided. In other specific examples, for instance a color canindicate any or all of: a size, such as a range of volumes correspondingto a suspected condition (e.g., hemorrhage); a type of condition; aseverity of a condition; and/or any other information.

S240 can optionally include prescribing a subset of images to be viewedby the recipient and/or an order in which images should be viewed (e.g.,the particular image shown first to the recipient upon opening a clientapplication in response to receiving a notification, the image shown inthe thumbnail of a notification, the only image or subset of imagessent, etc.). This can include, for instance, selecting the image orimages indicating the suspected condition (e.g., all slices containingthe suspected condition, a single slice containing the suspectedcondition, the slice containing the largest cross section of a suspectedcondition, the slice containing an important or critical feature of thesuspected condition, etc.) for viewing by the recipient. In specificexamples, the recipient (e.g., specialist) is sent a notificationwherein when the recipient opens the notification on a device (e.g.,mobile device), the image corresponding to the suspected condition isshown first (and optionally corresponds to a thumbnail image shown tothe recipient in the notification).

The notification(s) and/or image(s) provided to a recipient arepreferably provided within a threshold time period from the time inwhich the patient is imaged (e.g., 15 minutes, between 10 and 15minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, 5minutes, 3 minutes, 2 minutes, 1 minute, etc.), but can additionally oralternatively be provided in another suitable time frame (e.g., greaterthan 15 minutes), prior to a next action in the standard of care (e.g.,prior to a decision is made by a radiologist in a parallel workflow),and/or at any other time(s).

In some variations, S240 includes storing a dataset (e.g., at a remoteserver, at a local server, at a PACS server, etc.). In one example,metadata are extracted from the image data and stored separately fromimage data in a relational database. In another example, any or all ofthe data packet are stored (e.g., temporarily, permanently, etc.) to beused in one or more future analytics processes, which can function toimprove the method, better match patients with suitable treatmentoptions, or for any other suitable purpose.

S240 can optionally include applying a low bandwidth implementationprocess, which can function to reduce the time until a specialistreceives a first piece of data or data packet (e.g., an incompleteseries, incomplete study, single instance, single image, optimal image,image showing occlusion, etc.), reduce the processing required to informa specialist of a potential patient condition, reduce the amount of datarequired to be reviewed by a specialist, reduce the amount of data beingtransmitted from a remote computing system to a mobile device, orperform any other suitable function. The low bandwidth implementationprocess can include any or all of: organizing (e.g., chunking) data(e.g., chunking a series of images based on anatomical region),reordering data (e.g., reordering slices in a CT series), transmitting aportion (e.g., single image, single slice, etc.) of a data packet (e.g.,series, study, set of images, etc.) to a device (e.g., user device,mobile device, healthcare facility workstation, computer, etc.), sendingthe rest of the data packet (e.g., only in response to a request, aftera predetermined time has passed, once the data packet has been fullyprocessed, etc.), or any other process. In a specific example, forinstance, the image data (e.g., slices) received at a remote computingsystem from a scanner are chunked, reordered, and a subset of slices(e.g., a single slice) is sent to the device associated with aspecialist (e.g., prior to sending a remaining set of slices, in absenceof sending a remaining set of slices, etc.).

In some variations, S240 includes implementing a buffering protocol,which enables a recipient (e.g., specialist) to start viewing images(e.g., on a mobile device) prior to all of the images being loaded atthe device (e.g., user device, mobile device, etc.) at which therecipient is viewing images. Additionally or alternatively, thebuffering protocol can include transmitting images to the recipient inbatches, annotating images in batches, or otherwise implementing abuffering protocol.

Additionally or alternatively, S240 can include any other suitable stepsperformed in any suitable order.

The method can additionally or alternatively include any other suitablesub-steps for preparing the data packet.

In a first set of variations, S240 includes determining a subset of oneor more images conveying information related to a suspected patientcondition (e.g., showing the presence of the condition, showing thelargest region of the condition, showing a particular feature of thecondition, etc.); optionally annotating the images (e.g., to point outthe condition); and preparing a notification to be sent to thespecialist, wherein the notification instructs the specialist to view atleast the subset of images and optionally includes a thumbnail depictingone of the set of images which the specialist can view prior to viewingthe images.

4.7 Method—Transmitting Information to a Device Associated with the2^(nd) Point of Care S250

Transmitting information to a device associated with the 2^(nd) point ofcare (e.g., specialist, contact, etc.) S250 (e.g., as shown in FIG. 6 )functions to initiate a pull from a 1^(st) point of care to a 2^(nd)point of care, which can decrease time to care, improve quality of care(e.g., better match between patient condition and specialist), or haveany other suitable outcome. Preferably, the 2^(nd) point of care is ahub facility (e.g., specialist facility, interventional center,comprehensive stroke center, etc.). In some variations, the 1^(st) pointof care (e.g., healthcare facility at which patient initially presents)also functions as the 2^(nd) point of care, such as when a suitablespecialist is associated with the 1^(st) point of care, the 1^(st) pointof care is a hub (e.g., specialist facility, interventional center,comprehensive stroke center, etc.), it is not advised to transfer thepatient (e.g., condition has high severity based on a calculatedseverity score), or for any other reason.

S250 is preferably performed after (e.g., in response to) a 2^(nd) pointof care is determined, but can additionally or alternatively beperformed after a data packet (e.g., compressed data packet, encrypteddata packet, etc.) has been determined, multiple times throughout themethod (e.g., to multiple recipients, with multiple data packets, withupdated information, after a predetermined amount of time has passedsince a notification has been sent to a first choice specialist, etc.),or at any other time during the method 200.

The device is preferably a user device, further preferably a mobiledevice. Examples of the user device include a tablet, smartphone, mobilephone, laptop, watch, wearable device (e.g., glasses), or any othersuitable user device. The user device can include power storage (e.g., abattery), processing systems (e.g., CPU, GPU, memory, etc.), useroutputs (e.g., display, speaker, vibration mechanism, etc.), user inputs(e.g., a keyboard, touchscreen, microphone, etc.), a location system(e.g., a GPS system), sensors (e.g., optical sensors, such as lightsensors and cameras, orientation sensors, such as accelerometers,gyroscopes, and altimeters, audio sensors, such as microphones, etc.),data communication system (e.g., a WiFi module, BLE, cellular module,etc.), or any other suitable component.

The device is preferably associated with (e.g., owned by, belonging to,accessible by, etc.) a specialist or other individual associated withthe 2^(nd) point of care, but can additionally or alternatively beassociated with an individual or computing system at the 1^(st) point ofcare, the patient, or any other suitable individual or system.

In one variation, the device is a personal mobile phone of a specialist.In another variation, the device is a workstation at a healthcarefacility (e.g., first point of care, second point of care, etc.).

In some variations, information is sent to multiple members (e.g., allmembers) of a care team or clinical trial team, such as the care teamwho is treating, may treat the patient, or and/or will treat thepatient. This can enable the care team members to do any or all of: makea decision together (e.g., transfer decision, treatment decision, etc.);communicate together (e.g., through the client application); and/orperform any other function.

The information preferably includes a data packet, further preferablythe data packet prepared in S240. Additionally or alternatively, theinformation can include a subset of a data packet, the original datapacket, any other image data set, or any other suitable data. Theinformation further preferably includes a notification, wherein thenotification prompts the individual to review the data packet at thedevice (e.g., a message reciting “urgent: please review!”). Thenotification can optionally include a thumbnail with a selected image(e.g., image indicating patient condition), which a recipient can viewquickly, such as prior to the recipient unlocking device to which thenotification is sent. Additionally or alternatively, the notificationcan prompt the individual to review data (e.g., original data packet,uncompressed images, etc.) at a separate device, such as a workstationin a healthcare facility, a PACS server, or any other location. Furtheradditionally or alternatively, the notification can include any suitableinformation, such as, but not limited to: instructions (e.g., fortreating patient, directions for reaching a healthcare facility),contact information (e.g., for emergency physician at first point ofcare, administrative assistant, etc.), patient information (e.g.,patient history), or any other suitable information.

The notification preferably includes an SMS text message but canadditionally or alternatively include a message through a clientapplication (e.g., as described above, image viewing application,medical imaging application, etc.), an email message (e.g.,de-identified email), audio notification or message (e.g., recordingsent to mobile phone), push notification, phone call, a notificationthrough a medical platform (e.g., PACS, EHR, EMR, healthcare facilitydatabase, etc.), pager, or any other suitable notification.

One or more features of a notification can optionally convey a severityof the patient condition and/or an urgency of receiving a response froma recipient, which can function to adequately alert the recipient andproperly prioritize care of the patient (e.g., relative to otherpatients). In specific examples, for instance, an audio cue associatedwith a notification indicates an urgency of treating a patient, so thata recipient of the message knows to immediately review the images andtriage the patient.

The information is preferably sent to the device through a clientapplication executing on the user device but can additionally oralternatively be sent through a messaging platform, web browser, orother platform. In some variations, the information is sent to alldevices (e.g., mobile phone, smart watch, laptop, tablet, workstation,etc.) associated with the recipient (e.g., specialist), such as alldevices executing the client application associated with the recipient,which functions to increase the immediacy in which the recipient isnotified.

In one variation, S240 includes preparing a notification to be sent to adevice (e.g., user device, mobile device, etc.) associated with arecipient (e.g., a specialist), wherein the notification includes athumbnail indicating a selected image (e.g., compressed image showing asuspected condition), along with a message instructing the recipient toreview the images in a client application, and optionally the originalimages at a workstation afterward. In a first set of specific examples,upon detection that a read receipt has not been received (e.g., at theremote computing system) in a predetermined amount of time (e.g., 30seconds, 1 minute, 2 minutes, between 0 seconds and 2 minutes, 3minutes, between 2 minutes and 3 minutes, 5 minutes, greater than 5minutes, less than 10 minutes, etc.), a second notification istransmitted to a second recipient (e.g., a second specialist). In asecond set of specific examples, sending the notification furthertriggers and/or enables communication to be established among multiplemembers of a care team (e.g., a stroke team), such as through amessaging component of the client application, wherein the images can beviewed and discussed among the care team members. In a third set ofspecific examples, a notification is sent to specialist on a mobiledevice of the specialist, compressed images are previewed on thespecialist mobile device, and the specialist is notified as beingresponsible for viewing non-compressed images on a diagnostic viewer andengaging in appropriate patient evaluation and relevant discussion witha treating physician before making care-related decisions or requests.

In a second variation, S240 includes preparing a notification to be sentto a clinical trial research coordinator, such as a principalinvestigator, wherein the notification indicates that the patient is apotential candidate for a clinical trial (e.g., based on the detectionof a suspected condition, based on a set of clinical trial inclusioncriteria, etc.). In specific examples, a notification can be sent (e.g.,automatically, triggered by the principal investigator, etc.) to amembers of a clinical trial committee (e.g., physician committee),wherein approval is granted by the committee members (e.g., a majority,all, at least one, a predetermined number or percentage, etc.), such asthrough the client application.

4.8 Method—Receiving an Input from the Recipient

The method 200 can include receiving an input from the recipient, whichfunctions to determine a next step for the patient, and can include anyor all of: a confirmation of the suspected condition; a rejection of thesuspected condition (e.g., false positive); an acceptance by aspecialist and/or care team (e.g., stroke team) to treat the patient(e.g., at a 1^(st) point of care, at a 2^(nd) point of care, etc.); arejection of a specialist and/or care team to treat the patient; a readreceipt and/or an indication of a lack or a read receipt within apredetermined time threshold; an approval to enroll the patient in aclinical trial; additional clinical information entered by a physicianand/or other user; and/or any other suitable input.

In some variations, a notification is sent in S250 which prompts theindividual to provide an input, wherein the input can indicate that theindividual will view, has viewed, or is in the process of viewing theinformation (e.g., image data), sees the presence of a condition (e.g.,true positive, serious condition, time-sensitive condition, etc.), doesnot see the presence of a condition (e.g., false positive, seriouscondition, time-sensitive condition, etc.), has accepted treatment ofthe patient (e.g., swipes right, swipes up, clicks a check mark, etc.),has denied treatment of the patient (e.g., swipes left, swipes down,clicks an ‘x’, etc.), wants to communicate with another individual(e.g., healthcare worker at 1^(st) point of care), such as through amessaging platform (e.g., native to the device, enabled by the clientapplication, etc.), or any other input. In some variations, one or moreadditional notifications are provided to the individual (e.g., based onthe contents of the input), which can be determined by a lookup table,operator, individual, decision engine, or other tool. In one example,for instance, if the individual indicates that the condition is a truepositive, information related to the transfer of the patient (e.g.,estimated time of arrival, directions to the location of the patient,etc.) can be provided (e.g., in a transfer request, wherein patienttransfer to a specified location, such as the 2^(nd) point of care, canbe initiated upon transfer request receipt). In some variants, the data(e.g., images) are displayed on the user device (e.g., mobile device,workstation) in response to user interaction with the notification(e.g., in response to input receipt). However, the input can trigger anysuitable action or be otherwise used.

Additionally or alternatively, an input can automatically be receivedfrom the client application, such as a read receipt when the individualhas opened the data packet, viewed the notification, or interacted withthe client application in any other suitable way. In one example, if aread receipt is not received (e.g., at the remote computing system) fromthe device within a predetermined amount of time (e.g., 10 seconds), asecond notification and/or data packet (e.g., compressed set of images)are sent to a second individual (e.g., second choice specialist based ona lookup table).

In some variations, various outputs can be sent from the clientapplication (e.g., at the user device) to one or more recipients (e.g.,to a second user device, client application on a work station, on acomputing system, etc.), such as recipients associated with a firstpoint of care (e.g., radiologists, emergency physicians, etc.). Theoutputs can be determined based on the inputs received at the clientapplication associated with the individual (e.g., acceptance of case,verification of true positive, etc.), based on a lookup table, orotherwise determined. The outputs preferably do not alter the standardradiology workflow (e.g., are not shared with radiologists; radiologistsare not notified), which functions to ensure that the method 200 is atrue parallel process, and that the standard radiology workflow resultsin an independent assessment of the patient, but can additionally oralternatively cut short a workflow, bring a specialist in on the patientcase earlier than normal, or affect any other process in a healthcarefacility.

The outputs can include any or all of: the suspected condition,parameters (e.g., volume of an ICH) and/or scores (e.g., severity score,urgency score, etc.) associated with the suspected condition; theselection of one or more recipients of a notification (e.g., establishedand/or proposed care team of the patient); a proposed and/or confirmedintervention for the patient (e.g., type of procedure); an updatedstatus (e.g., location, health status, intervention status, etc.) of oneor more patients (e.g., a centralized list of all patients beingreviewed by and/or treated by a specialist; a consent of the patient(e.g., for a clinical trial); an estimated parameter of the patient(e.g., estimated time of arrival at a second point of care); and/or anyother suitable outputs.

4.9 Method—Initiating Treatment of the Patient

The method can additionally or alternatively include initiatingtreatment (e.g., transfer) of the patient, wherein the treatment caninclude any or all of the treatment options described above, such as ayor all of: a point of care (e.g., remain at 1^(st) point of care, betransferred to a 2^(nd) point of care, etc.) at which the patient willbe treated; a procedure to treat the suspected condition; a specialistand/or care team to be assigned to the patient; a clinical trial inwhich to enroll the patient; and/or any other suitable treatments.

In variations involving recommending the patient for a clinical trial,initiating treatment of the patient can include receiving arecommendation that the patient be considered for a clinical and/orresearch trial, based on one or more of: a suspected clinical conditionof the patient (e.g., ICH), patient information (e.g., demographicinformation), a patient's willingness or potential willingness toparticipate, and/or any other suitable information. Initiating therecommendation can include transmitting any or all of the notificationsdescribed above (e.g., text message, call, email, etc.) to a specialistinvolved in the clinical and/or research trial, a specialist who hasactively turned on notifications for clinical trial recruitment, aresearcher, a research principal investigator, an administrativeassistant, the patient himself, or any other suitable entity orindividual.

4.8 Method—Aggregating Data S260

The method 200 can optionally include any number of sub-steps involvingthe aggregation of data involved in and/or generated during the method200, which can function to improve future iterations of the method 200(e.g., better match patients with a specialist, decrease time to treat apatient, increase sensitivity, increase specificity, etc.). Theaggregated data is preferably used in one or more analytics steps (e.g.,to refine a treatment option, make a recommendation for a drug orprocedure, etc.), but can additionally or alternatively be used for anyother suitable purpose.

In some variations, for instance the outcomes of the patients examinedduring the method 200 are recorded and correlated with theircorresponding data packets, which can be used to assess the success ofthe particular treatment options chosen and better inform treatmentoptions in future cases.

4.9 Variations

In one variation of the system 100, the system includes a router no,which operates at a computing system at a 1^(st) point of care andreceives image data from an imaging modality. The router transmits theimage data to a remote computing system, wherein a series of algorithms(e.g., machine learning algorithms) are performed at the remotecomputing system, which determines a hypothesis for whether or not asuspected condition (e.g., ICH) is present based on the image dataand/or any associated metadata. Based on the determination, a contact isdetermined from a lookup table (e.g., in storage at the remote computingsystem), wherein the contact is notified at a user device (e.g.,personal device) and sent image data through a client applicationexecuting on the user device. One or more inputs from the contact at theapplication can be received at the remote computing system, which can beused to determine a treatment option (e.g., next point of care) for thepatient. However, the system and/or components thereof can be used inany other suitable manner.

In a first variation of the method 200, the method functions to augmenta standard radiology workflow operating in parallel with the method,which can include any or all of: at a remote computing system (e.g.,remote from the first point of care), receiving a set of images (e.g.,of a brain of the patient), wherein the set of images is concurrentlysent to the standard radiology workflow operating in parallel with themethod and automatically detecting a condition (e.g., ICH) from the setof images. Upon condition detection, the method can include any or allof, automatically: determining a second specialist from the standardradiology workflow, wherein the specialist is associated with a secondpoint of care; notifying the second specialist on a mobile deviceassociated with the second specialist before the radiologist notifiesthe first specialist; displaying a compressed version of the set ofimages on the mobile device; and initiating a pull of the patient (e.g.,from the 1^(st) point of care to the 2^(nd) point of care, into aclinical trial protocol, from the 1^(st) point of care to a clinicaltrial at a later date, as initiated by a specialist at the 2^(nd) pointof care, as initiated by a specialist at the 1^(st) point of care, asinitiated by a principal investigator and/or other research coordinatorof a clinical trial, etc.).

In a specific example (e.g., as shown in FIG. 7 ), the method includes,at a remote computing system, receiving a set of Digital Imaging andCommunications in Medicine (DICOM) brain images associated with thepatient, wherein the set of DICOM brain images is concurrently sent to astandard radiology workflow operating in parallel with the method. Inthe standard radiology workflow, the radiologist analyzes the set ofDICOM brain images and notifies a specialist based on a visualassessment of the set of DICOM brain images at the workstation, whereinthe standard radiology workflow takes a first amount of time. The methodcan then include detecting an ICH from the set of DICOM brain images,which includes any or all of: identifying an image dataset of a headfrom an NCCT scan; registering the set of images against a reference setof images, wherein an output of the registration is a straightened andresized image dataset; windowing the registered image dataset based on apredetermined range of Hounsfield units, wherein a minimum pixel valuecorresponds to a Hounsfield unit just below soft matter and wherein amaximum pixel value corresponds to a Hounsfield unit just above bone;assigning regions having a maximum pixel value (e.g., 255, value lessthan 255, etc.) to have a minimum pixel value (e.g., 0, greater than 0,etc.); normalizing the set of voxel values in the image data (e.g., tohave a mean Hounsfield unit value of 24.5 HU, to have a standarddeviation Hounsfield unit value of 39.5 HU, etc.); forming a 3D arraycontaining a probability value (e.g., between 0 and 1) for each voxel inthe image data, the probability value indicating a likelihood that thevoxel represents a portion of an ICH; converting this probabilisticoutput into a binary mask defined to be all voxels having a probabilityscore above a predetermined threshold (e.g., above 0.5, at least 0.5,between 0.3 and 1, above 0.3, less than 0.5, etc.); performing a firstpost-processing subroutine to assess each slice of the segmentationmask, wherein components of the segmentation mask below a size threshold(e.g., volume less than 0.4 mL) and/or less than a predetermined numberof voxels away (e.g., less than 3 voxels away) from a bone voxel areeliminated from further consideration; performing a secondpost-processing subroutine to assess the segmentation mask (e.g., withremaining components after the 1^(st) subroutine, with all componentsfrom the original segmentation mask, etc.) in 3D, wherein components ofthe segmentation mask having a predetermined size (e.g., thickness of 1voxel, thickness less than a predetermined threshold, etc.) areeliminated from further consideration; determining that a component ofsegmentation mask remains after the 1^(st) and 2^(nd) subroutines;determining that the patient has a suspected ICH; providing anotification (e.g., through texting) to a specialist associated with a2^(nd) point of care (e.g., different from the 1^(st) point of care,within the 1^(st) point of care, etc.); sending a compressed imagedataset to the specialist; displaying a high resolution image dataset ona workstation of the specialist; and triggering an action based on inputfrom the specialist (e.g., transfer of patient from the 1^(st) point ofcare to the 2^(nd) point of care, recommending the patient for aclinical trial, etc.).

In a second variation of the method 200 (e.g., as shown in FIG. 10 ),additional or alternative to the first, the method 200 includes:receiving a set of images taken of a body region of a patient and takena point of care; transmitting the set of images to a remote computingsystem; sorting and/or checking that the set of images satisfypredetermined inclusion criteria based on the metadata associated withthe set of images; preprocessing the set of images, which includes awindowing process and a normalization process and optionally aregistration process; segmenting a region from the set of images througha set of deep learning models, which includes assigning a set ofprobability scores to a set of voxels of the images and creating abinary mask based on the set of probability scores (e.g., allprobability scores greater than 0.5); checking for exclusion criteria,such as a size criteria; determining a set of one or more outputs suchas the determination of a suspected condition and a recipient with whichto share images and notify to review the images; transmitting thenotification and any suitable accompanying information to a device ofthe one or more recipients; receiving an input from the recipient; andtriaging and/or otherwise treating the patient based on the input.

In a set of specific examples, the method further includes establishingcommunication between users (e.g., texting, call, HIPAA-complianttexting, HIPAA-compliant calling, video call, etc.), such as between anyor all of: multiple healthcare workers (e.g., physicians, surgeons,etc.), multiple research coordinators (e.g., from the same clinicaltrial, from different clinical trials, etc.), a healthcare worker and aresearch coordinator (e.g., for the research coordinator to askquestions from the surgeon, as shown in FIG. 15 , etc.), a researchcoordinator and a patient (e.g., to submit a consent form to thepatient, to receive a consent form from the patient, etc.), a healthcareworker and a patient, and/or between any other suitable users andindividuals.

In a third variation of the method 200 (e.g., as shown in FIG. 11 , asshown in FIG. 13 , as performed in accordance with a system shown inFIG. 14 , etc.), additional or alternative to those described above, themethod functions to evaluate if a patient presenting with a potentialpathology (e.g., stroke, ICH, LVO, etc.) qualifies for a clinical trialand if so, to alert (e.g., automatically, in a time period shorter thana determination made by a radiologist in a standard radiology workflow,etc.) a research coordinator (e.g., principal investigator) associatedwith the clinical trial, wherein the method includes: receiving a datapacket comprising a set of images (e.g., NCCT images of a brain of thepatient) sampled at the first point of care, wherein the data packet isoptionally concurrently sent to the standard radiology workflow;determining an anatomical feature (e.g., large vessel region) from theset of images; determining a parameter (e.g., calculating a volume,calculating a centerline length, etc.) of the feature; comparing theparameter (e.g., centerline length) with set of clinical trial criteria(e.g., inclusion criteria, exclusion criteria, etc.); and in an eventthat the parameter satisfies the clinical trial criteria (e.g.,according to a set of thresholds), presenting a notification on a mobiledevice associated with the research coordinator (e.g., as shown in FIG.12 ). If the research coordinator decides to include the patient in theclinical trial (e.g., based on the notification, based on a set ofcompressed images sent to a user device of the research coordinator,based on a calculated parameter, based on a consensus reached by aclinical trial committee in communication with the research coordinator,etc.), the research coordinator or other user or entity can optionallytransmit a consent form to the patient (e.g., to a user device of thepatient, to a workstation associated with the 1^(st) point of care, to aworkstation associated with the 2^(nd) point of care, etc.) and/or to ahealthcare worker (e.g., to a user device of the healthcare worker, to aworkstation of the healthcare worker, etc.), such as a surgeon,associated with the patient (e.g., via a client application executing ona user device of the research coordinator, from a remote computingsystem, etc.). Additionally or alternatively, the research coordinatorcan communicate (e.g., via a HIPAA-compliant messaging platformestablished through the client application, through a text messagingapplication, etc.) with a physician associated with the patient, and/orotherwise review and communicate information.

Additionally or alternatively, the method can include any or all of:providing a mobile image viewer at a client application with visibleprotected health information after secure login by the recipient;providing a patient status tracker to keep the recipient informed ofupdates to the patient; providing case volume information, which candetect and alert recipients about patients throughout the hub and spokenetwork that can benefit from a particular treatment (e.g.,neurosurgical treatment); providing bed capacity information to arecipient, which enables increased access to early surgical intervention(e.g., thereby leading to improved patient outcomes, decreased hospitallength of stay, decreased ventilator use, etc.); and/or any otherprocesses.

Further additionally or alternatively, the method can include any otherprocesses performed in any suitable order.

Although omitted for conciseness, the preferred embodiments includeevery combination and permutation of the various system components andthe various method processes, wherein the method processes can beperformed in any suitable order, sequentially or concurrently.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. A method for detecting a suspected hemorrhage, the methodcomprising: receiving a set of images associated with a patient;processing the set of images with a set of trained models to identify aparticular region from the set of images, wherein producing the set oftrained models comprises: performing a first training process,comprising training the set of trained models based on a first set ofimages labeled as positive with respect to a hemorrhage and a second setof images labeled as negative with respect to a hemorrhage; andperforming a second training process, comprising training the set oftrained models based on a set of outputs of the first training process,the set of outputs comprising a set of false positive hemorrhagedeterminations; in response to detecting the suspected hemorrhage basedon processing the set of images, triggering an action, the actionoperable to improve an efficiency of triage of the patient.
 2. Themethod of claim 1, wherein the action is triggered at a user deviceassociated with a specialist.
 3. The method of claim 2, wherein the userdevice is a personal mobile user device of the specialist.
 4. The methodof claim 2, wherein the action triggers assignment of treatment of thepatient to the specialist.
 5. The method of claim 4, wherein the actioncomprises a notification at the user device.
 6. The method of claim 1,wherein receiving the set of images, processing the set of images, andtriggering the action are performed during triage of the patient.
 7. Themethod of claim 6, wherein the patient initially arrives at a firstpoint of care.
 8. The method of claim 7, wherein the specialist isassociated with a second point of care, the second point of care remotefrom the first point of care.
 9. The method of claim 1, wherein thesecond training process is performed after the first training process.10. A system for detecting a suspected hemorrhage, the systemcomprising: a set of trained models, wherein the set of trained modelsis produced with: a first training process, wherein in the firsttraining process, the set of trained models is trained based on a firstset of images labeled as positive with respect to a hemorrhage and asecond set of images labeled as negative with respect to a hemorrhage;and a second training process, wherein in the second training process,the set of trained models is trained based on a set of false positivehemorrhage determinations; a computing subsystem, wherein the computingsubsystem: receives a third set of images associated with a patient; andprocesses the third set of images with the set of trained models toidentify a particular region from the third set of images; and a clientapplication executable on a user device of a recipient, wherein theclient application: triggers an action in response to detecting thesuspected hemorrhage based on processing the third set of images, theaction operable to improve an efficiency of triage of the patient. 11.The system of claim 10, wherein the particular region comprises asegmented hyperdense region in the third set of images, whereindetecting the suspected hemorrhage comprises determining that a volumeof the segmented hyperdense region exceeds a predetermined threshold.12. The system of claim 10, wherein the set of false positive hemorrhagedeterminations is produced as a set of outputs of the first trainingprocess.
 13. The system of claim 10, wherein the second training processis performed after the first training process.
 14. A method fordetecting a suspected hemorrhage, the method comprising: training a setof models to produce a trained set of models, comprising training theset of models based on: a first set of images having a first set oflabels, the first set of labels indicating a presence of a hemorrhage; asecond set of images having a second set of labels, the second set oflabels indicating an absence of a hemorrhage; and a third set of imageshaving a third set of labels, the third set of labels indicating a falsepositive detection of a hemorrhage; receiving a fourth set of imagesassociated with a patient; processing the fourth set of images with thetrained set of models; and triggering an action in response detectingthe suspected hemorrhage based on processing the fourth set of images,the action operable to improve an efficiency of triage of the patient.15. The method of claim 14, wherein the fourth set of images is imagedat a first point of care, and wherein the action comprises an initiationof a transfer of the patient to a second point of care, the second pointof care remote from the first point of care.
 16. The method of claim 15,wherein the action is triggered automatically.
 17. The method of claim14, wherein the action triggers transmission of a notification to aspecialist located at a 2^(nd) point of care remote from a 1^(st) pointof care at which the patient is located, wherein an input from thespecialist in response to the notification triggers an assignment oftreatment of the patient to the specialist.
 18. The method of claim 14,wherein the third set of images is a subset of the first set of images,and wherein the third set of labels replaces the first set of labelsoriginally determined for the subset of the first set of images.
 19. Themethod of claim 18, wherein training the set of models comprises a firsttraining process, wherein in the first training process, the set ofmodels is trained based on the first and second sets of images, whereinin the second training process, the set of models is trained based onthe third set of images, and wherein the second training process isperformed after the first training process.
 20. The method of claim 14,wherein processing the fourth set of images comprises determining aseverity score associated with the suspected hemorrhage, wherein theaction is determined at least in part based on the severity score.